a.Structure data (tabular data,etc...)

b.Unstructured data (images,text,audio,etc...)

c.semi structured data (XML,JSON,etc...)

a.qualitative (nominal,ordinal,binary) 

b.quantitative(discrete,continuous)

https://www.chi2innovations.com/blog/discover-data-blog-series/data-types-101/

1.Beautifulsoup  https://www.freecodecamp.org/news/how-to-scrape-websites-with-python-and-beautifulsoup-5946935d93fe/

  mechanicalsoup   https://analyticsindiamag.com/mechanicalsoup-web-scraping-custom-dataset-tutorial/

2.Scrapy,PyScrappy,Pandas Datareader,Instaloader,lxml

3.Selenium     https://www.freecodecamp.org/news/better-web-scraping-in-python-with-selenium-beautiful-soup-and-pandas-d6390592e251/

4.Request to access data 

5.AUTOSCRAPER - https://github.com/alirezamika/autoscraper https://www.youtube.com/watch?v=9BQ353Yu1D0 https://www.analyticsvidhya.com/blog/2021/04/automate-web-scraping-using-python-autoscraper-library/

scrapeasy  Scrape Any Website in Seconds with One Line of Code  https://github.com/joelbarmettlerUZH/Scrapeasy

Scrap Images From E-Commerce Website Using AutoScraper https://www.analyticsvidhya.com/blog/2021/05/scrap-images-from-e-commerce-website-using-autoscraper-library/

amazon auto scraper library https://webautomation.io/

 Listly https://www.listly.io/r/stdfr

FiftyOne Now easier to download and evaluate  https://towardsdatascience.com/googles-open-images-now-easier-to-download-and-evaluate-with-fiftyone-615ce0482c02

webbot https://pypi.org/project/webbot/

gazpacho https://github.com/maxhumber/gazpacho

html_scraper_streamlit_app https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be

6.Twitter scraping tool (𝚝𝚠𝚒𝚗𝚝 or tweepy or tweetlib)-https://github.com/twintproject/twint

  twitterscraper https://www.youtube.com/watch?v=MpIi4HtCiVk
  
  twython https://github.com/ryanmcgrath/twython
  
  twarc https://github.com/DocNow/twarc https://scholarslab.github.io/learn-twarc/01-quick-start.html
  
  snscrape  extract twitterr data  https://github.com/JustAnotherArchivist/snscrape
  
  Scweet A simple and unlimited twitter scraper  https://github.com/Altimis/Scweet
  
  GetOldTweets3,GoogleNews,snscrape,GetOldTweets3

  Scrape Twitter for Tweets https://github.com/taspinar/twitterscraper
  
  HAR File Web Scraper https://stevesie.com/har-file-web-scraper https://www.youtube.com/watch?v=LcqVDfueb8g

  https://analyticsindiamag.com/complete-tutorial-on-twint-twitter-scraping-without-twitters-api/
  
  https://developer.twitter.com/en/docs
  
  pytrends  https://medium.com/nerd-for-tech/scraping-data-from-online-platforms-to-enhance-time-series-forecasts-6eec3c68636d
  
  Scraping Instagram -instaloader  https://thecleverprogrammer.com/2020/07/30/scraping-instagram-with-python/
  
  Instascrape   
  
  Scrape LinkedIn Profiles with ProxyCurl API
  
  Reddit Dataset  Using PSAW and PRAW in Python
  
  Scraping Reddit using Python Reddit API Wrapper  (PRAW)
  
  Scrape Wikipedia  wikipedia https://www.thepythoncode.com/article/access-wikipedia-python
  
  patang - Scrape Product details from eCommerce Sites with Puppeteer and DOM String  https://www.youtube.com/watch?v=3sgxRmyOuXs
  
  Download Wikipedia https://www.wikidata.org/wiki/Wikidata:Main_Page https://www.youtube.com/watch?v=hC1rY4lRY0s https://towardsdatascience.com/an-efficient-way-to-read-data-from-the-web-directly-into-python-a526a0b4f4cb
  
  Web Scraping to Create a CSV File  https://thecleverprogrammer.com/2020/08/08/web-scraping-to-create-csv/
  
  Amazon Web Scraper, Amazon Auto Scraper

7.urllib

8.pattern

9.Octoparse Easy Web Scraping   https://www.octoparse.com/

 prowebscraper https://prowebscraper.com/features

 Web scraper https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn?hl=en

 ParseHub https://www.parsehub.com/  https://analyticsindiamag.com/parsehub-no-code-gui-based-web-scraping-tool/
 
 PyScrappy https://github.com/mldsveda/PyScrappy https://www.analyticsvidhya.com/blog/2022/02/web-scraping-with-pyscrappy/
 
 Gazpacho  https://github.com/maxhumber/gazpacho
 
 ScrapeSimple Website: https://www.scrapesimple.com
 
 Content Grabber https://contentgrabber.com/Manual/understanding_the_concept.htm
 
 Crawly https://crawly.diffbot.com/ 
 
 Apify https://apify.com/
 
 Mozenda Website: https://www.mozenda.com/
 
 obsei https://github.com/lalitpagaria/obsei
 
 Diffbot  https://analyticsindiamag.com/diffbot/
 
 Trustpilot,webhose,scrapingbot 
 
 lxml  https://lxml.de/index.html#introduction
 
 ScrapingBee  https://analyticsindiamag.com/scrapingbee-api/
 
 Scrape HTML tables https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be  or pd.read_html
 
 requests-html https://github.com/kennethreitz/requests-html
 
 newspaper https://github.com/codelucas/newspaper  https://www.youtube.com/watch?v=Hfry5XnISyc
 
 newspaper3k: https://newspaper.readthedocs.io  # easily extract text from articles
 
 newscatcher https://github.com/kotartemiy/newscatcher https://www.youtube.com/watch?v=pHzOuizZq4I
 
 patang (extract product details) https://github.com/tejazz/patang
 
 lisc https://github.com/lisc-tools/lisc
 
 Helena WEB AUTOMATION FOR END USERS https://helena-lang.org/
 
 pandas(read_html)
 
 wget,curl,parsehub,webhouse,octoparse,scraping bot,scraping bee,Common,Content Grabber,Docparser,Scraper API,Import.io,Altair Monarch,WebAutomation.io,WebScraper.io,Scrape.do, AvesAPI, ParseHub, Import.io, Octoparse, Scrapingdog, Diffbot, ScrapingBee, Grepsr, Scraper API, Scrapy

 Crawl Crawly  https://crawly.diffbot.com/   

 HTML basics for web scraping,Web Scraping with Octoparse,Web Scraping with Selenium

 10-best-web-scraping-tools  https://www.scraperapi.com/blog/the-10-best-web-scraping-tools/
 
 https://www.kdnuggets.com/2021/02/6-web-scraping-tools.html
  
 https://analyticsindiamag.com/complete-learning-path-to-web-scraping-with-all-major-tools/ https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d
 
 https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d https://www.kdnuggets.com/2018/02/web-scraping-tutorial-python.html
 
 https://www.octoparse.com/ https://github.com/tirthajyoti/pydbgen https://www.mozenda.com/ https://www.mockaroo.com/ https://lionbridge.ai/ https://www.mturk.com/ https://appen.com/
 
 11.GoogleImageCrawler,google_images_download,bing_image
 
 https://www.freepik.com/popular-photos , https://stocksnap.io/ , https://www.pexels.com/ ,https://unsplash.com/ , https://pixabay.com/

Awesome list of datasets in 100+ categories https://www.kdnuggets.com/2021/05/awesome-list-datasets.html

https://sebastianraschka.com/blog/2021/ml-dl-datasets.html  https://enoumen.com/2021/04/23/data-sciences-datasets-data-visualization-data-analytics-big-data-data-lakes/

https://serokell.io/blog/best-machine-learning-datasets https://medium.com/@ODSC/25-excellent-machine-learning-open-datasets-940ca2124dfc  

1)kaggle-https://www.kaggle.com/datasets , 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔𝚊𝚐𝚐𝚕𝚎𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜

Downloading Kaggle datasets directly into Google Colab -https://towardsdatascience.com/downloading-kaggle-datasets-directly-into-google-colab-c8f0f407d73a

How to Download Kaggle Datasets using Jupyter Notebook https://www.analyticsvidhya.com/blog/2021/04/how-to-download-kaggle-datasets-using-jupyter-notebook/

2)https://sebastianraschka.com/blog/2021/ml-dl-datasets.html

movielens-https://grouplens.org/datasets/movielens/latest/

dagshub datset https://dagshub.com/explore/datasets

100+ of the Best Free Data Sources For Your Next Project https://www.columnfivemedia.com/100-best-free-data-sources-infographic/

World and national data, maps & rankings https://knoema.com/atlas/sources

3)data.gov-https://data.gov.in/

4)uci-https://archive.ics.uci.edu/ml/datasets.php     https://github.com/tirthajyoti/UCI-ML-API

5)Group Lens dataset https://grouplens.org/

Wikipedia ML Datasets https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research

AWS Open Data Registry,data.gov (portals),YELP Open dataset,UNICEF Dataset,Big Bad NLP Database,Microsoft Dataset

6)world3bank  https://data.world/ , worldbank

7)Google Cloud BigQuery public datasets

  Google Public Datasets-cloud.google.com/bigquery/public-data/
  
  Google Cloud Data Catalog  https://cloud.google.com/data-catalog
  
  Academic Torrents-https://academictorrents.com/check.htm?returnto=%2Fbrowse.php

8)online hacktons

 Datasets  https://www.paperswithcode.com/datasets

9)image data from google_images_download

https://www.visualdata.io/discovery

http://xviewdataset.org/#dataset

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html

10)image data from Bing_Search

image data from simple_image_download  https://github.com/RiddlerQ/simple_image_download

11)https://www.columnfivemedia.com/100-best-free-data-sources-infographic

graviti  Unleash the Power of Unstructured Data  https://www.graviti.com/?utm_medium=0730Ismael

12)Reddit:https://lnkd.in/dv5UCD4       https://www.reddit.com/r/datasets/

praw.Reddit https://github.com/praw-dev/praw

13)https://datasets.bifrost.ai/?ref=producthunt

14)data.world:https://lnkd.in/gEK897K

15)https://data.world/datasets/open-data

   https://tinyletter.com/data-is-plural

16)FiveThirtyEight :-  https://lnkd.in/gyh-HDj , https://data.fivethirtyeight.com/

17)BuzzFeed :- https://lnkd.in/gzPWyHj

   Buzzfeed News -github.com/BuzzFeedNews
   
   Socrata - https://opendata.socrata.com/

18)Google public datasets :- https://lnkd.in/g5dH8qE

Statistics Canada https://www.statcan.gc.ca/eng/start  https://towardsdatascience.com/how-to-collect-data-from-statistics-canada-using-python-db8a81ce6475

Deep Image Search AI-based image search engine https://github.com/TechyNilesh/DeepImageSearch

https://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free

19)Quandl :- https://www.quandl.com  stock data

   statista : https://www.statista.com/ stock data

20)socorateopendata :- https://lnkd.in/gea7JMz

21)AcedemicTorrents :- https://lnkd.in/g-Ur9Xy

22) Automates Image Annotation for Deep Learning Models https://medium.com/towards-artificial-intelligence/improving-data-labeling-efficiency-with-auto-labeling-uncertainty-estimates-and-active-learning-5848272365be

Label Studio,Sloth,LabelBox,TagTog,Amazon SageMaker GroundTruth,Playment,Superannotate,Playment,Dataturk,LightTag,Superannotate,CVAT,sloth,LabelImg,cvat

Automate data preparation https://www.superb-ai.com/

https://neptune.ai/blog/annotation-tool-comparison-deep-learning-data-annotation?utm_source=linkedin&utm_medium=post&utm_campaign=blog-annotation-tool-comparison-deep-learning-data-annotation

Diffgram,Label Studio ,CVAT,SuperAnnotate,Datasaur https://anthony-sarkis.medium.com/the-5-best-ai-data-annotation-platforms-for-machine-learning-2021-ec17c15142f3

https://foobar167.medium.com/open-source-free-software-for-image-segmentation-and-labeling-4b0332049878

***Label Assist: Model Assisted Pre-Annotation for Computer Vision https://blog.roboflow.com/announcing-label-assist/ https://www.youtube.com/watch?v=919CihTlkZw&feature=youtu.be***

https://github.com/jsbroks/awesome-dataset-tools

makeml https://makeml.app/

superannotate https://www.superannotate.com/

jupyter-innotater data annotator for Jupyter notebooks https://github.com/ideonate/jupyter-innotater

JupyterLab extension for annotating data  https://github.com/explosion/jupyterlab-prodigy

semi-auto-image-annotation-tool https://github.com/virajmavani/semi-auto-image-annotation-tool

labelimage:- https://github.com/wkentaro/labelme  ,  https://github.com/tzutalin/labelImg 

labelCloud  lightweight tool for labeling 3D bounding boxes in point clouds https://github.com/ch-sa/labelCloud

labeller https://www.labellerr.com/

prodigy Radically efficient machine teaching An annotation tool powered by active learning  https://prodi.gy/

Labelbox-https://labelbox.com/

Playment-https://playment.io/

SuperAnnotate -https://www.superannotate.com/

CVAT-https://github.com/openvinotoolkit/cvat

Lionbridge- https://lionbridge.ai/

LinkedAI: A No-code Data Annotations- https://analyticsindiamag.com/linkedai/

Dataturks

V7 Darwin The Rapid Image Annotator   https://docs.v7labs.com/docs/loading-a-dataset-in-python   https://github.com/v7labs/darwin-py#usage-as-a-python-library

https://waliamrinal.medium.com/top-and-easy-to-use-open-source-image-labelling-tools-for-machine-learning-projects-ffd9d5af4a20

https://github.com/heartexlabs/awesome-data-labeling  

Label a Dataset with a Few Lines of Code https://eric-landau.medium.com/label-a-dataset-with-a-few-lines-of-code-45c140ff119d

https://analyticsindiamag.com/complete-guide-to-data-labelling-tools/ https://neptune.ai/blog/data-labeling-software

Extraction of Objects In Images and Videos Using 5 Lines of Code https://towardsdatascience.com/extraction-of-objects-in-images-and-videos-using-5-lines-of-code-6a9e35677a31

https://neptune.ai/blog/data-labeling-software?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-data-labeling-software

23)tensorflow_datasets as tfds  https://www.tensorflow.org/datasets  (import tensorflow_datasets as tfds)

https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/

24)https://datasets.bifrost.ai/?ref=producthunt

25)https://ourworldindata.org/

26)https://data.worldbank.org/

27)google open images:https://storage.googleapis.com/openimages/web/download.html

30 Largest TensorFlow Datasets for Machine Learning  https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/

https://cloud.google.com/bigquery/public-data/   https://towardsdatascience.com/bigquery-public-datasets-936e1c50e6bc  

https://christopherzita.medium.com/how-to-download-google-images-using-python-2021-82e69c637d59

28)https://data.gov.in/

29)imagenet dataset-http://www.image-net.org/

30)https://parulpandey.com/2020/08/09/getting-datasets-for-data-analysis-tasks%e2%80%8a-%e2%80%8aadvanced-google-search/

31)https://storage.googleapis.com/openimages/web/index.html  , 

   https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F09qck
   
   https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset&_ga=2.35328417.1459465882.1589693499-869920574.1589693499
   
   https://catalog.data.gov/dataset?groups=education2168#topic=education_navigation
   
   https://vincentarelbundock.github.io/Rdatasets/datasets.html
 
32)coco dataset https://cocodataset.org/#explore
 
33)huggingface datasets-https://github.com/huggingface/datasets  https://huggingface.co/datasets  https://huggingface.co/languages

pip install datasets

34)Big Bad NLP Database-https://datasets.quantumstat.com/

fast.ai Datasets https://course.fast.ai/datasets

https://github.com/niderhoff/nlp-datasets

600 NLP Datasets and Glory  https://pub.towardsai.net/600-nlp-datasets-and-glory-4b0080bf5ab

nlp-datasets https://github.com/karthikncode/nlp-datasets

https://analyticsindiamag.com/15-most-important-nlp-datasets/      https://medium.com/ai-in-plain-english/25-free-datasets-for-natural-language-processing-57e407402c60

35)https://www.edureka.co/blog/25-best-free-datasets-machine-learning/

36)bigquery public dataset ,Google Public Data Explorer

https://cloud.google.com/public-datasets        https://guides.library.cmu.edu/machine-learning/datasets

37)inbuilt library data eg:iris dataset,mnist dataset,etc...

pandas-datareader  https://github.com/pydata/pandas-datareader

tf.data.Datasets for TensorFlow Datasets 

38)https://data.gov.sg/    https://data.gov.au/   https://data.europa.eu/euodp/en/data   https://data.europa.eu/euodp/en/data    https://data.govt.nz/

data.gov.be ,data.egov.bg/ ,data.gov.cz/english ,portal.opendata.dk,govdata.de,opendata.riik.ee,data.gov.ie,data.gov.gr,datos.gob.es,data.gouv.fr,data.gov.hr

dati.gov.it,data.gov.cy,opendata.gov.lt,data.gov.lv,data.public.lu,data.gov.mt,data.overheid.nl,data.gv.at,danepubliczne.gov.pl,dados.gov.pt,data.gov.ro,podatki.gov.si

data.gov.sk,avoindata.fi,oppnadata.se,https://data.adb.org/ ,https://data.iadb.org/ ,https://www.weforum.org/agenda/2018/03/latin-america-smart-cities-big-data/

https://data.fivethirtyeight.com/ , https://wiki.dbpedia.org/ ,https://www.europeandataportal.eu/en ,https://data.europa.eu/ ,https://www.census.gov/,

https://www.who.int/data/gho ,https://data.unicef.org/open-data/ ,http://data.un.org/ ,https://data.oecd.org/ ,https://data.worldbank.org/  

39.Awesome Public Dataset- https://github.com/awesomedata/awesome-public-datasets

Get OpenML’s Dataset in One Line of Code https://mathdatasimplified.com/2021/04/23/fetch_openml-get-openmls-dataset-in-one-line-of-code/

https://github.com/the-pudding/data

datasets  https://github.com/benedekrozemberczki/datasets

kdnuggets  https://www.kdnuggets.com/datasets/index.html

Hub https://github.com/activeloopai/Hub

40.Datasets for Machine Learning on Graphs-https://ogb.stanford.edu/

41.https://www.johnsnowlabs.com/data/

42.30 largest tensorflow datasets-https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/

43. coco dataset-https://cocodataset.org/#home

flickr-downloader https://github.com/renatoviolin/flickr-downloader/

Google Open images-https://opensource.google/projects/open-images-dataset  https://storage.googleapis.com/openimages/web/index.html

50+ Object Detection Datasets-https://medium.com/towards-artificial-intelligence/50-object-detection-datasets-from-different-industry-domains-1a53342ae13d

70+ Image Classification Datasets from different Industry domains-https://medium.com/towards-artificial-intelligence/70-image-classification-datasets-from-different-industry-domains-part-2-cd1af6e48eda

VisualData Discovery https://www.visualdata.io/discovery   https://guides.library.cmu.edu/machine-learning/datasets
 
 data https://storage.googleapis.com/openimages/web/visualizer/index.html?c=%2Fm%2F04yqq2&r=false&set=train&type=segmentation&utm_campaign=Weekly%20Machine%20Learning%20news&utm_medium=email&utm_source=Revue%20newsletter
 
VisualData https://www.visualdata.io/discovery
   
bifrost-   https://datasets.bifrost.ai/

satellite images https://towardsdatascience.com/finding-satellite-images-for-your-data-science-project-888695361925

https://public.roboflow.com/

https://www.visualdata.io/discovery        http://www.image-net.org/      https://www.cs.toronto.edu/~kriz/cifar.html  
   
tensorflow_datasets.object_detection - https://storage.googleapis.com/openimages/web/index.html

https://github.com/google-research-datasets/Objectron/  https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html?m=1 

http://idd.insaan.iiit.ac.in/   http://database.mmsp-kn.de/koniq-10k-database.html

https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html

https://www.visualdata.io/discovery  https://blogs.bing.com/maps/2019-03/microsoft-releases-12-million-canadian-building-footprints-as-open-data

https://blogs.bing.com/maps/2019-09/microsoft-releases-18M-building-footprints-in-uganda-and-tanzania-to-enable-ai-assisted-mapping

https://datasets.bifrost.ai/     https://storage.googleapis.com/openimages/web/download.html  https://computervisiononline.com/datasets  http://yacvid.hayko.at/

https://www.cogitotech.com/use-cases/biodiversity/

ImageNet data -http://image-net.org/

ApolloScape Dataset-http://apolloscape.auto/

https://github.com/chrieke/awesome-satellite-imagery-datasets

44.https://github.com/fivethirtyeight/data

45.Recommender Systems Datasets-https://cseweb.ucsd.edu/~jmcauley/datasets.html

46.indiadataportal-https://indiadataportal.com/

47.US Government Open Dataset: https://www.data.gov/

https://censusreporter.org/   https://data.census.gov/cedsci/

48.AWS Public Data Sets:https://registry.opendata.aws/    https://aws.amazon.com/opendata/?wwps-cards.sort-by=item.additionalFields.sortDate&wwps-cards.sort-order=desc

49.https://the-eye.eu/public/AI/pile_preliminary_components/

  Reddit -https://www.reddit.com/r/datasets/  
  
  wikipedia-https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
  
  http://opendata.cern.ch/  ,  https://www.imf.org/en/Data
  
  Global Health Observatory data repository-https://apps.who.int/gho/data/node.main
  
  CERN Open Data Portal-http://opendata.cern.ch/
  
  TensorFlow Datasets https://www.tensorflow.org/datasets
  
50.openblender- https://www.openblender.io/#/welcome

51.Top 10 Datasets For Cybersecurity Projects- https://analyticsindiamag.com/top-10-datasets-for-cybersecurity-projects/

52.Datasets from Web Crawl Data (nlp)-http://data.statmt.org/cc-100/

53.https://www.springboard.com/blog/free-public-data-sets-data-science-project/

54.NASA - https://nasa.github.io/data-nasa-gov-frontpage/ace 

55.Academic Torrents,GitHub Datasets,CERN Open Data Portal,Global Health Observatory Data Repository

56.32 Data Sets to Uplift your Skills in Data Science-https://blog.datasciencedojo.com/data-sets-data-science-skills/?utm_content=144243072&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012

https://lionbridge.ai/datasets/the-50-best-free-datasets-for-machine-learning/

57.OpenDaL-https://opendatalibrary.com/

Data Is Plural-https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit#gid=0

VisualData-https://www.visualdata.io/discovery

https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f
 
58.Pandas Data Reader-https://pandas-datareader.readthedocs.io/en/latest/remote_data.html

59.ieee-dataport-https://ieee-dataport.org/datasets

https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f

https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/data/datasets.md#datasets-and-sources-of-raw-data

60.Generating Realistic Fake Data https://towardsdatascience.com/free-resources-for-generating-realistic-fake-data-da63836be1a8

Full Synthetic Data ,Partial Synthetic Data,Hybrid Synthetic Data

Faker is a Python package that generates fake data-https://github.com/joke2k/faker

ydata-synthetic,Gretel,gretel-synthetics,GenerateData,DataSynthesizer,SDV,SDGym,SDMetrics,Copulas,gretel-synthetics,kubric,CTGAN,Synthea,synthia,nbsynthetic ,pydbgen,synthpop,faker,Tonic,ydata,Mostly AI,Mirry.ai,Hazy,Gretel,Diveplane,Datagen,Mimesis,faker,FauxFactory,Radar,PikaAccelario,Chooch,Datagen,Datomize,Deep Vision Data,Monitaur,MOSTLY AI,OpenSynthetics,Replica Analytics,Scale AI,SKY ENGINE AI,Synthesis AI,Plaitpy,TimeseriesGenerat,Accelario,Chooch,dgutils,AI.Reverie,Kinetic Vision,SynthDet,OpenSynthetics,Mockaroo,GenerateData,JSON Schema Faker,FakeStoreAPI,Mock Turtle,nbsynthetic,AiFi,AI.Reverie,Anyverse,Cvedia,DataGen,Diveplane,Gretel,Hazy,Mostly AI,OneView,TRGD,YDATA Synthetic,SDV,Tonic.AI,Mostly.AI,Parallel Domain,Mindtech,Synthesis AI,Oneview,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic, kubric,Stable Diffusion,Parallel Domain,Mindtech,Synthesis AI,Oneview,MOSTLY AI,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic,MOSTLY AI, GenRocket, YData, Hazy, and MDClone ,Gretel, MOSTLY AI, Hazy, Statice ,NVIDIA Omniverse, OneView, CVEDIA, Datagen, Parallel Domain,Infinity AI,Parallel Domain,Rendered.AI,Scale.AI,SKY ENGINE AI,Synthesis AI,Paella,statice,DataSynthesizer,Pydbgen,TimeseriesGenerator,Mimesis,Synthesized,Syntheticus,Syntho,Tonic,Clearbox AI ,RDT (Reversible Data Transforms),DeepEcho

Models: GANs, CTGAN, WGAN, WGAN-GP, VAEs,GANs, TimeGAN, AR 

GAN-based Deep Learning data synthesizer   CTGAN,CopulaGAN,Synthetic Data Vault,Probabilistic AutoRegressive model

Extract the metadata using DataDescriber, Compare the input and synthetic data using ModelInspector

Mockaroo  https://www.mockaroo.com/ 

GenerateData  https://site.generatedata4.com/ 

JSON Schema Faker  https://json-schema-faker.js.org/ 

FakeStoreAPI  https://fakestoreapi.com/

graviti dataset https://gas.graviti.com/open-datasets

Synthetic data for computer vision https://github.com/ZumoLabs/zpy

GANs for Tabular Synthetic Data Generation https://github.com/Diyago/GAN-for-tabular-data

Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/

Synthetic structured data generators https://github.com/ydataai/ydata-synthetic

gretel Synthetic Data API  https://gretel.ai/

Timeseries DGAN https://synthetics.docs.gretel.ai/en/latest/models/timeseries_dgan.html

DatasetGAN: an automatic procedure to generate massive datasets of high-quality images 

Generating synthetic tabular data with GANs,Synthetic Time-Series Data by A GAN approach

Unity Launches Synthetic Image Datasets https://www.marktechpost.com/2021/04/23/unity-launches-synthetic-image-datasets-to-train-ai-and-computer-vision-models-faster/

Generate Your Own Dataset using GAN https://www.analyticsvidhya.com/blog/2021/04/generate-your-own-dataset-using-gan/

accurate of synthetic data https://gretel.ai/blog/how-accurate-is-my-synthetic-data

Synthetic data library https://github.com/finos/datahub https://github.com/agmmnn/awesome-blender https://opendata.blender.org/ https://www.youtube.com/watch?v=eZwOeBkLL8E

https://www.kdnuggets.com/2019/09/scikit-learn-synthetic-dataset.html

Fully Synthetic Data,Partially Synthetic Data ,Hybrid Synthetic Data https://towardsdatascience.com/synthetic-data-key-benefits-types-generation-methods-and-challenges-11b0ad304b55

Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/  https://dockship.io/articles/607847e461373d1b994cc2dc/create-synthetic-images-using-opencv-(python)

gretel-synthetics  Synthetic data generators for structured and unstructured text, featuring differentially private learning.  https://github.com/gretelai/gretel-synthetics

Synthetic Data Generation Using Gaussian Mixture Model https://deepnote.com/@chanakya-vivek-kapoor/Synthetic-Data-Generation-QaaTRs73T2iCb0amHFbwpQ

Synthetic Data Vault  https://analyticsindiamag.com/guide-to-synthetic-data-vault-an-ecosystem-of-synthetic-data-generation-libraries/ https://github.com/sdv-dev/SDV

Create Your own Image Dataset using Opencv https://www.analyticsvidhya.com/blog/2021/05/create-your-own-image-dataset-using-opencv-in-machine-learning/

ydata-synthetic https://github.com/ydataai/ydata-synthetic

Table Evaluator About Evaluate real and synthetic datasets with each other  https://github.com/Baukebrenninkmeijer/table-evaluator

evaluate quality and efficacy of synthetic datasets SDMetrics https://github.com/sdv-dev/SDMetrics

61.Text Data Annotator Tool - Datasaur  https://datasaur.ai/

Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing  https://www.tagalog.ai/tagalog/

62.Google Analytics cost data import https://segmentstream.com/google-analytics?utm_source=twitter&utm_medium=cpc&utm_campaign=ga_costs_import_en&utm_content=guide

63.https://lionbridge.ai/services/crowdsourcing/    https://lionbridge.ai/     https://www.clickworker.com/  https://appen.com/  https://www.globalme.net/

64.Azure Open Datasets https://azure.microsoft.com/en-us/services/open-datasets/       https://azure.microsoft.com/en-in/services/open-datasets/catalog/
  
Yelp Open Dataset  https://www.yelp.com/dataset

https://data.world/

ODK Open Data Kit- https://getodk.org/

World Bank Open Data https://data.worldbank.org/

https://analyticsindiamag.com/10-biggest-data-breaches-that-made-headlines-in-2020/

https://data.mendeley.com/

https://github.com/iamtekson/geospatial-data-download-sites

https://eugeneyan.com/writing/data-discovery-platforms/

65.https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f

https://towardsdatascience.com/data-repositories-for-almost-every-type-of-data-science-project-7aa2f98128b

https://github.com/MTG/freesound-datasets

https://dataform.co/

https://github.com/rfordatascience/tidytuesday https://www.youtube.com/watch?v=vCBeGLpvoYM

https://www.analyticsvidhya.com/blog/2020/12/top-15-datasets-of-2020-that-every-data-scientist-should-add-to-their-portfolio/?utm_source=linkedin&utm_medium=AV|link|high-performance-blog|blogs|44181|0.375

https://cseweb.ucsd.edu/~jmcauley/datasets.html

66.https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research  

https://archive.org/details/datasets

https://commoncrawl.org/

https://www.youtube.com/watch?v=1aUt8zAG09E

67. 6 Sources of Financial Data https://medium.datadriveninvestor.com/financial-data-431b75975bb

yfinance for finance data using     https://github.com/ranaroussi/yfinance  https://medium.com/towards-artificial-intelligence/algorithmic-trading-with-python-and-machine-learning-part-1-47c56706c182

import fix_yahoo_finance as yf , yahoofinancials ,Pandas DataReaders,Twelve Data

financeapi https://towardsdatascience.com/pull-and-analyze-financial-data-using-a-simple-python-package-83e47759c4a7

Investing.com pip install investpy ,Kite by Zerodha pip install kiteconnect,quandl  pip install quandl

https://www.analyticsvidhya.com/blog/2021/01/bear-run-or-bull-run-can-reinforcement-learning-help-in-automated-trading/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

Downloading Historical Stock prices with Alpha Vantage  https://medium.com/towards-artificial-intelligence/downloading-historical-stock-prices-with-alpha-vantage-688edad46a6d

Pandas Datareader https://pandas-datareader.readthedocs.io/en/latest/  https://www.youtube.com/watch?v=f2BCmQBCwDs

Get Financial Data Directly into Python https://www.quandl.com/tools/python   https://medium.com/nerd-for-tech/how-to-get-financial-data-using-python-7a508f25fc39

openml https://www.openml.org/search?type=data

https://registry.opendata.aws/

voice_datasets https://github.com/jim-schwoebel/voice_datasets
 
Dynamically-Generated-Hate-Speech-Dataset https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset

68.DOCANO, an open source text annotation tool  https://github.com/doccano/doccano

69.https://www.dataquest.io/blog/free-datasets-for-projects/

70.audio set https://research.google.com/audioset/

71.FlatData Flat explores how to make it easy to work with data in git and GitHub https://octo.github.com/projects/flat-data?utm_campaign=Data_Elixir&utm_source=Data_Elixir_337

72.Snorkel is an open-source Python library for programmatically building training datasets without manual labeling. https://www.snorkel.org/  https://towardsdatascience.com/snorkel-programmatically-build-training-data-in-python-712fc39649fe

 Types of missing value  https://datamuni.com/@atsunorifujita/missing-value-imputation-using-datawig
 
 Handling Missing Values in Pandas https://pub.towardsai.net/handling-missing-values-in-pandas-f87cec928937
 
 Identify the source of missing data
 
 i.missing completely at random(no correlation b/w missing and observed data) we can delete no disturbance of data distribution
 
 ii.missing at random (randomness in missing data, missing value have correlation by data) we can't delete because disturbance of data distribution
 
 iii.missing not at random  (there is reason for missing value and directly related to value)
 
 iv.structured missing  100 % sure on why it is missing
 
 Identify Missingness Types With Missingno  https://towardsdev.com/how-to-identify-missingness-types-with-missingno-61cfe0449ad9
 
 Univariate,Multivariate  https://medium.com/fintechexplained/what-are-imputers-in-data-science-b72f8308322b
 
 univariate imputation impute on 1 column  multi variate imputation impute on 1 or more column

 1.if missing data too small then delete it a.row deletion b.column deletion c.pairwise deletion and listwise deletion
 
 Drop based on a threshold value,Drop using a subset of columns
 
 2.replace by statistical method mean(influenced by outiler),median(not influenced by outiler),mode , minimum, maximum,Zero,Constant
 
 Fill with Mean / Median of Column or  Group   Forward Fill  or Forward Fill within Groups  
 
 Mean and Median Fill with Groupby 
 
 Pass another DataFrame to fillna function to fill up the missing values.  
 
 Similar case Imputation
  
 3.apply classifier algorithm to predict missing value
 
 Using Algorithms that support missing values
 
 Imputation using Deep Learning Library — Datawig  https://github.com/awslabs/datawig
 
 4.Simple Imputer,and Multiple Imputation ,Iterative imputer,knn imputer, multivariate imputation, Verstack — NaNImputer,Impyute —MICE ,Substitution
 
 5.apply unsupervised 
 
 6.Random Imputation,Iterative Imputation,Random Sample imputation
 
 7.Adding a variable to capture NAN(missing term),Imputation with the string ‘Missing’,Adding missing idicator
 
 8.Arbitrary Value Imputation
 
 9.hot deck Imputation,Cold deck imputation
 
 10.regression Imputation,Stochastic Regression Imputation,Interpolation and Extrapolation
 
 11.End of Distribution Imputation
 
 12.Arbitrary Value Imputation
 
 13.Frequent Category Imputation
 
 14.MICE Imputation,miceforest ( https://github.com/AnotherSamWilson/miceforest )
 
  Miss Forest https://github.com/stekhoven/missForest
 
 15.interpolation  https://www.analyticsvidhya.com/blog/2021/06/power-of-interpolation-in-python-to-fill-missing-values/   Interpolate  or  Interpolate within Groups
 
  LINEARINTERPOLATION ,POLYNOMIALINTERPOLATION,INTERPOLATION THROUGH PADDING
 
 Extrapolation and Interpolation ,Time-Based Interpolation,Spline Interpolation,Linear Interpolation,Smoothing, interpolation,Bidirectional Recurrent Imputation for Time Series (
 
 16.Last Observation Carried Forward (LOCF)  ,  Next Observation Carried Backward , Rolling Statistics, Interpolation
 
 Single and Multiple Imputation,Univariate Imputation,Multivariate Imputation ,Iterative Imputer,MissForest Imputation,Stochastic Regression Imputation, Multiple Imputations, Datawig, Hot-Deck imputation, Extrapolation, Interpolation
 
 datawig  Imputation of missing values in tables  https://github.com/awslabs/datawig
 
 Imputation using K-NN,missForest,Random Forest-based Imputation,missingpy,som,Ann,mlp
 
 Model based procedure gaussian mixture model
 
 Imputation Using Deep Learning (Datawig),neural network for imputation,BRITS
 
 15.autoimpute-https://github.com/kearnz/autoimpute
 
 16.bfill / ffill      Back Fill   or  Back Fill within Groups
 
 17.Adding a variable to capture NAN
 
 18.replace NAN with a new category
 
 19.Missing indicator
 
 After drop or imputation feature distribution should be same
 
 https://www.kdnuggets.com/2021/05/deal-with-categorical-data-machine-learning.html
 
 https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779
 
 https://stefvanbuuren.name/fimd/want-the-hardcopy.html  https://www.datasciencecentral.com/profiles/blogs/how-to-treat-missing-values-in-your-data-1
 
 20.Imputation with the string ‘Missing’ ,Addition of binary missing indicators
      
 21.Algorithms robust to missing values - LightGBM
 
 datawig imputation https://github.com/awslabs/datawig
 
 22.Cluster-based approach for missing value imputation Naive clustering,Column-sensitive clustering
 
 Top Data Cleaning Tools https://www.marktechpost.com/2022/02/20/top-data-cleaning-tools-for-data-science-and-machine-learning-projects-in-2022/
 
 OpenRefine https://openrefine.org/ https://github.com/OpenRefine/OpenRefine
 
 Data Ladder https://dataladder.com/
 
 re-data  fix data issues https://github.com/re-data/re-data
 
 Automatically find and fix errors in your ML datasets. https://github.com/cleanlab/cleanlab
 
 Clean APIs for data cleaning https://github.com/pyjanitor-devs/pyjanitor
 
 datacleaner https://github.com/rhiever/datacleaner
 
 https://github.com/akanz1/klib  https://pyjanitor-devs.github.io/pyjanitor/ https://dataprep.ai/  https://scrubadub.readthedocs.io/en/latest/index.html  https://www.bitrook.com/
 
 AutoClean https://github.com/elisemercury/AutoClean

Dora,PrettyPandas,DataCleaner,Tabulate,Pyjanitor

 1.Under Sampling - mostly not prefer because lost of data  imbalaced-learn,tomek links,Random Under-Sampling, Edited Nearest Neighbours,NearMiss
 
 Random majority under-sampling with replacement,Tomek Links Undersampling,Under-sampling with Cluster Centroids,Condensed Nearest Neighbour,One-Sided Selection,Neighboorhood Cleaning Rule,One-Sided Selection,
 
 2.Over Sampling  (RandomOverSampler (here new points create by same dot)) ,  SMOTETomek(new points create by nearest point so take long time),BorderLine Smote,Borderline-SMOTE SVM,FAIR SMOTE,DBSMOTE,SMOTE-ENN ,KMeans Smote,SVM Smote,SMOTe NC,ENNSMOTE,SVMSMOTE,MOTE-N ADASYN,ADASYN,Smote-NC,Random Over Sampling,RandomUnderSampler,SMOTEN,SMOTE-Tomek,SMOTE-ENN,SMOTE-CUT,Cluster-Based Over Sampling, Informed Over Sampling,MSMOTE,Oversampling Using Gaussian Mixture Models,SMOTE + Tomek Links, SMOTE + ENN,Crucio SMOTEENN,NearMiss,OSS & NCR — under sampling,Borderline SMOTE KNN,Borderline SMOTE SVM,Adaptive Synthetic Sampling (ADASYN),BalancedBaggingClassifier() ,  BalancedRandomForestClassifier  SMOTE-NC  
 
 Over-sampling followed by under-sampling : SMOTE + Tomek links,SMOTE + ENN 
 
 smote_variants https://github.com/analyticalmindsltd/smote_variants

 https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5
 
 https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/
 
 ensmble based -Bagging Based techniques, Boosting-Based techniques,Adaptive Boosting- Ada Boost techniques,Gradient Tree Boosting,XG Boost 
 
 tools Imb-learn,SMOTE-Variants,Regression-ReSampling  https://towardsdatascience.com/tools-to-handle-class-imbalance-bff20c3bf099

 Balancing data sets with Crucio ADASYN  https://medium.com/softplus-publication/balancing-data-sets-with-crucio-adasyn-79f04ff0779d

 LoRAS: A Better Oversampling Algorithm https://analyticsindiamag.com/hands-on-guide-to-loras-a-better-oversampling-algorithm/  https://github.com/narek-davtyan/LoRAS   
 
 https://towardsdatascience.com/7-over-sampling-techniques-to-handle-imbalanced-data-ec51c8db349f
 

 Combining Over and Under-sampling
 
 3.class_weight give more importance(weight) to that small class ( Cost-Sensitive Algorithms)
 
 from sklearn import compute_class_weight  
 
 Cost-sensitive learning,Class-balanced loss,Focal loss
 
 weighted loss function
 
 4.use Stratified kfold to keep the ratio of classess constantly, train teat spilt startify attribute
 
 Use K-fold Cross-Validation in the Right Way,Stratified Cross Validation,repeated K-fold Cross-Validation,Stratified K-fold Cross-Validation
 
 Stratified Sampling,Stratified splits
 
 5.Weighted Neural Network
 
 cluster based sampling 
 
 6.MESA https://analyticsindiamag.com/guide-to-mesa-boost-ensemble-imbalanced-learning-with-meta-sampler/
 
 7.choose  Proper Evaluation Metric metric roc,f1,etc...
   
 https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/  https://www.kdnuggets.com/2020/01/5-most-useful-techniques-handle-imbalanced-datasets.html
 
 8.Deep Imbalanced Regression https://github.com/YyzHarry/imbalanced-regression https://analyticsindiamag.com/deep-imbalanced-regression-complete-guide/
 
 Imbalanced Dataset Sampler  https://github.com/ufoym/imbalanced-dataset-sampler
 
 9.Ensemble Techniques ensemble techinque - Bagging Based techniques,Boosting-Based techniques
 
 BalancedBaggingClassifier,Threshold moving,Easy Ensemble classifier,Balanced Random Forest,Balanced Bagging,RUSBoost,MESA
 
 10.Try Different Algorithms (ensemble techinque - Bagging Based techniques,Boosting-Based techniques)
 
 model based (some models are particularly suited for imbalanced dataset)
 
 Algorithmic Ensemble Techniques,Tree-Based Algorithms
 
 11.Try a Different Perspective ( consider as anomaly detection or change detection)
 
 Threshold Moving Methods,One-Class Classification,Customised Ensemble Algorithms
 
 Probability Tuning Algorithms,Calibrating Probabilities,Tuning the Classification Threshold
 
 12.databalancer  https://github.com/pradeepdev-1995/databalancer
 
 13.collect more data 
 
 14.treat problem as anomaly detection
 
 15.Combined Class Methods
 In this type of method, various methods are fused together to get a better result to handle imbalance data. For instance, like SMOTE can be fused with other methods like MSMOTE (Modified SMOTE), SMOTEENN (SMOTE with Edited Nearest Neighbours), SMOTE-TL, SMOTE-EL, etc. to eliminate noise in the imbalanced data sets
 
 16.One-Class Algorithms,One-Class Support Vector Machines,Isolation Forests,Minimum Covariance Determinant,Local Outlier Factor,Mahalanobis Distance for One Class Classification
 
 17.BalancedBatchGenerator https://imbalanced-learn.org/stable/references/generated/imblearn.keras.BalancedBatchGenerator.html
 
 18.train_test_split stratify attribute  , stratify split
 
 19.  https://github.com/pradeepdev-1995/databalancer
 
      Meta’s balance package https://github.com/facebookresearch/balance

 Discretisation plus categorical encoding,Discretisation plus encoding Discretisation with classification trees,Domain knowledge discretisation
  
 Data Binning
 
 Binning based on distribution (quantile-cut),Binning based on values (cut)
 
 Bucketing  , quantile bucketing ,Clipping

 1.One Hot Encoding , dummy, and effect coding,Similarity Encoding,Binary Encoding
 
 Rainbow Method for Label Encoding
 
 2.Count Or Frequency Encoding 
 
 3.Ordinal encoding,Nominal Encoding,Monotonic ordinal encoding,Target Guided Ordinal Encoding,Target Guided Mean Encoding,Target-Mean-Encoding
 
 4.Target encoding / Mean encoding,GapEncoder,MinHashEncoder,Target guided ordinal encoding,Bayesian Target Encoding
 
 Target Encoding,K-Fold Target Encoding,Leave-One-Out Target Encoding,Leave One fold out Target Encoding,Target Encoding with a Weighted Mean
 
 5.Probability Ratio Encoding,Rank Encoding,Polynomial Encoding,Backward Difference Encoding
 
 6.label encoding  or .cat.codes ,Label Encoding with Rainbow Method
 
 7.probability ratio encoding 
 
 8.woe(Weight_of_evidence)
 
 Word2Vec(word Word embedding)
 
 9.one hot encoding with multi category (keep most frequently repeated only) (One hot encoding of top categories)
 
 10.feature hashing,CatBoost Encoding 
 
 11.sparse csr matrix
 
 12.entity embeddings,Categorical Embeddings
 
 13.binary encoding,Base-N Encoding
 
 14.Rare label encoding
 
 15.Leave-one-out(Loo) encoding,Generalized Linearn Mixed Model

 16.hash encoding,MinHashEncoder,SimilarityEncoder,DatetimeEncoder,SuperVectorizer,FeatureHasher,DictVectorizer,HashingVectorizer,DecisionTreeEncoder
 
 17.dummy encoding,NaN Encoding,bin counting scheme,effect coding scheme
 
 18.Helmert Encoding,Backward Difference Encoding,James-Stein Encoding,M-estimator Encoding,Thermometer Encoder,Bayesian Encoders,Effect Encoding

 Helmert Encoding,Base N Encoding,Hash Encoding,Effect or Sum or Deviation Encoding,Backward Difference Encoding,M-Estimator Encoding,James- Stein Encoding,Thermometer Encoding,CatBoost Encoding,Backward Difference Encoding,Binary Encoding,NaN encoding Polynomial encoding,Expansion encoding,Probability Ratio,Binary encoding,cat boost encoder,glm encoder,m-estimte,sum coding, polynomial  Encoding,PRatioEncoder,DecisionTreeEncoder,Similarity Encoding,BackwardDifferenceEncoder GapEncoder,MinHashEncoder,TargetEncoder,Polynomial Encoding,James-Stein Encoding,MultiLabelBinarizer,SumEncoder,Quantile Encoder,Summary Encoder ,Base N Coding
 
 Transform your categorical columns with imperio SmoothingTransformer
 
 entity encoder for categorical variable   https://contrib.scikit-learn.org/category_encoders/
 
 Automatically selects the best encoder https://github.com/dirty-cat/dirty_cat
 
 Improve ML Model Performance by Combining Categorical Features https://towardsdatascience.com/improve-ml-model-performance-by-combining-categorical-features-a23efbb6a215
 
 https://towardsdatascience.com/beyond-one-hot-17-ways-of-transforming-categorical-features-into-numeric-features-57f54f199ea4
 
 https://towardsdatascience.com/how-to-encode-categorical-data-d44dde313131  https://towardsdatascience.com/python-for-finance-7-useful-libraries-that-you-should-know-e422b9e9aaba

   1.Normalisation  

   2.Standardization(StandardScaler)
 
   3.Robust Scaler not influenced by outliers because using of median,IQR
   
   4.Min Max Scaling
   
   5.Mean normalization
   
   6.maximum absolute scaling
   
   7.Power Transformer Scaler
   
   8.Scaling To Median And Quantiles,Scaling to minimum and maximum values,Scaling to the vector norm
   
   9.unit vector scaler
   
   10.Z-score standardization
   
   https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/?utm_source=linkedin&utm_medium=KJ|link|high-performance-blog|blogs|44204|0.375

       b.Logarithmic Transformation,LogCpTransformer
    
       c.Reciprocal Trnasformation
    
       d.Square Root Transformation
    
       e.Exponential Transdormation 
    
       f.BoxCOx and Yeo-Johnson Transformation
    
       g.log(1+x) Transformation
       
       h.johnson
       
       i.power transformations  https://towardsdatascience.com/when-and-how-to-use-power-transform-in-machine-learning-2c6ad75fb72e
       
       g.Quantile Transformation ,Arcsin Transformation , Inverse of Log,Inverse of Exponential,Inverse of Square Root,Square of Log,Square root of Exponential
       
       Root transformation,Cube root transformation,Cosine Transformation,SplineTransformer,FunctionTransformer,ArcsinTransformer 
       
       Left skewness (use powers) Squares transformation,Cubes transformation,High powers

  2 type of outilers available: Global outiler(single value/data point that deviates from the distribution), Local outiler,Contextual (conditional) outliers,Collective outliers(Group of datapoint deviates from the distribution)

  eg: incase of fraud detection outilers are very important
  
  methods to find outiler: Tukey’s fences ,KNN distance,Autoencoders,Standard Deviation,zscore,boxplot,scatter plot,histogram,Violin Plot,IQR,TensorFlow_Data_Validation,svm,One-Class SVM,Isolation Forest,kmeans,DBSCAN,K Means Clustering,Percentile,knn,autoencoder,local outiler factor,One-Class Classification,Medıan Absolute Devıatıon
  
  Automatic Outlier Detection:Isolation Forest,DBSCAN,Local Outlier Factor,Standard Deviation Approach,K Means Clustering,Minimum Covariance Determinant,Robust Random Cut Forest,DBScan Clustering,One-Class Classification,One-Class SVM,Autoencoder,Outlier Detection using In-degree Number,Histogram-based Outlier Detection,Robust Covariance,PyNomaly,angle-based outlier detection (ABOD),k-Nearest Neighbors Detector,Elliptic Envelope,Cluster-based,Local Outlier Factor,Histogram-based Outlier Detection
  
  outiler treatment: Keep them,mean/median/random imputation,drop,discretization (binning),Winsorization,treat as seperate group,replace with resperctive percentiles,standardize and scale the data,transforation(log,scaling,sqrt,power),Replace the outlier values with a suitable value (Like 3rd deviation),Percentile Based Flooring and Capping,Binning,Trimming,Treating outliers as missing values,Top/bottom/zero coding,winsorizing,robust scaler,log transformation,binning,regularisation,Discretization,arbitrary value
  
  Outlier capping with IQR Outlier capping with mean and std Outlier capping with quantiles Arbitrary capping
  
  Separation: If the amount of the outlier is higher than the normal then we can separate them from the main data and fit the model on them separately
  
  Use a Different algorithm that is not sensitive to outliers
  
  Segment data so outliers are in a separate group 
  
  Weighted means (which put more weight on the “normal” part of the distribution)
  
  Trimming: Remove outliers from dataset. However, it can remove large proportion of data.
  Capping: No data is removed. However, it distorts variable distribution. 
  Missing data: The outliers are treated as missing data.
  Discretization: The outliers are put into lower and upper bins. 
  Arbitrary capping: Domain knowledge of the variable is required to cap the min and max 
  
  ML model which are not sensitive to outliers Like:-KNN,Decision Tree,SVM,NaïveBayes,Ensemble 
  
  PyOD: A Python Toolkit For Outlier Detection https://analyticsindiamag.com/guide-to-pyod-a-python-toolkit-for-outlier-detection/ 
  
  TODS: An Automated Time-series Outlier Detection System https://github.com/datamllab/tods https://towardsdatascience.com/tods-detecting-outliers-from-time-series-data-2d4bd2e91381
  
  anomalib anomaly detection library https://github.com/openvinotoolkit/anomalib
  
  if outiler present then use robust scaling
  
  alibi-detect https://github.com/SeldonIO/alibi-detect#adversarial-detection   https://docs.seldon.io/projects/alibi-detect/en/latest/
  
  https://medium.com/towards-artificial-intelligence/outlier-detection-and-treatment-a-beginners-guide-c44af0699754
  
  https://towardsdatascience.com/two-outlier-detection-techniques-you-should-know-in-2021-1454bef89331

 Types of Anomalies : Point anomalies,Contextual anomalies,Collective anomalies,Group Anomalies,Spatial Anomalies,Temporal Anomalies

 clustering techniques to find it
 
 Timetk https://towardsdatascience.com/timetk-the-r-library-for-time-series-analysis-9822f7720318
 
 Isolation Forest(for Big Data),Z score,dbscan,Local Outlier Factor,One-Class Support Vector Machine,Autoencoders,knn,Time Series Analysis,Elliptic EnvelopeInterquartile Range,Median Absolute Deviation,K-Nearest Neighbours,Fast-MCD,Auto Encoders,K-means,Histogram-based,pca,K-means,Gaussian Mixture Model,Autoencoder,Hidden Markov Models (HMM)

  𝐏𝐲𝐎𝐃
  Local Correlation Integral (LCI),Histogram-based Outlier Detection (HBOS),Angle-based Outlier Detection (ABOD),Clustering-Based Local Outlier Factor (CBLOF),Minimum Covariance Determinant (MCD),Stochastic Outlier Selection (SOS),Spectral Clustering for Anomaly Detection (SpectralResidual),Feature Bagging,Average KNN,Connectivity-based Outlier Factor (COF),Variational Autoencoder (VAE)
 
 bootstrapping to remove the influence of the outlier data
 
 Anomaly detection using PyOD  https://pyod.readthedocs.io/en/latest/   https://www.youtube.com/watch?v=QPjG_313GOw  https://github.com/yzhao062/pyod https://pyod.readthedocs.io/en/latest/pyod.models.html
 
 ADBench https://github.com/Minqi824/ADBench
 
 Anomaly Detection Pyfbad https://github.com/Teknasyon-Teknoloji/pyfbad
 
 divided into three types:Point/Global Anomalies,Collective Anomalies,Contextual Anomalies https://towardsdatascience.com/a-comprehensive-beginners-guide-to-the-diverse-field-of-anomaly-detection-8c818d153995
 
 https://github.com/zhuyiche/awesome-anomaly-detection
 
 https://medium.com/@ODSC/data-sciences-role-in-anomaly-detection-bd976f93d7e3

 Random Sampling,Systematic Sampling,Cluster Sampling,Weighted Sampling,Stratified Sampling
 
 a.biased sampling
 
 b.unbiased sampling

  a.Combination of multiple features with mathematical operations
  
  b.Combination of multiple features with a reference value

Explore the dataset by using  python or microsoft Excel,Atoti,Power BI,Datapane’s,Tableau,TabPy,SAS Business Intelligence and Analytics Tool,QlikView,PyToQlik ,KNIME,Splunk,RapidMiner,Zoho Analytics,Sisense etc...

TabPy: Combining Python and Tableau https://www.kdnuggets.com/2020/11/tabpy-combining-python-tableau.html

atoti https://www.atoti.io/  https://www.youtube.com/watch?v=Hb6mSXa14oo   Datapane’s Create a Beautiful Dashboard in Python in a Few Lines of Code https://towardsdatascience.com/datapanes-new-features-create-a-beautiful-dashboard-in-python-in-a-few-lines-of-code-a3c44523292b

Switching from Spreadsheets to Neptune.ai https://neptune.ai/blog/switching-from-spreadsheets-to-neptune-ai

Data Analysis using excel https://www.excel-easy.com/data-analysis.html https://www.educba.com/data-analysis-tool-in-excel/ https://www.youtube.com/watch?v=OOWAk2aLEfk

Power BI In Jupyter Notebooks https://github.com/microsoft/powerbi-jupyter  https://analyticsindiamag.com/microsoft-releases-power-bi-in-jupyter-notebooks/

Mito Generating Python By Editing Spreadsheet  https://www.youtube.com/watch?v=yy3-C39ra6s https://trymito.io/?source=twitter1

Automate Pivot Table with Python https://towardsdatascience.com/automate-excel-with-python-pivot-table-899eab993966

OpenPyXL: A Python Module For Excel https://analyticsindiamag.com/guide-to-openpyxl-a-python-module-for-excel/

causal interactive dashboards and beautiful visuals https://www.causal.app/,

Visual Programming (Orange) https://orange.biolab.si/

Integrating Tableau With Python https://analyticsindiamag.com/tabpy/  Qlib https://analyticsindiamag.com/qlib/

Data visualization (Matplotlib,Seaborn,DASH,Plotly,Plotly-Express,pyqtgraph,Bokeh,Pandas-Bokeh,Pygal,hvplot,holoviews,chartify,lets-plot,pyqtgraph,glue,plotnine,pygal,bqplot,toyplot,chart,itkwidgets,vedo,ipyvolume,pyvista,glumpy,geopandas,pycountry,geopy,geo-py,pypopulation,  geotext,folium,cartopy,gmplo,ipyleaflet,geoviews,geoplot,splot,arviz, hypertools,geoplotlib,Geopandas package,choroplethmaps,Leafmap,Dash,Pydot,Geoplotlib,ggplot,visualizer,Greppo,Altair,folium,geoplot,networkx,graphviz,pydot,pygraphviz,python-igraph,pyvis,pygsp,ipycytoscape,nxviz ipydagred3,Diffbot,etc...)

Dashboarding : bokeh,dash,streamlit,panel,visdom ,voila,wave,jupyter-flex,ipyflex,pandas_bokeh   

Openpxl: Automate Excel Reporting  Datapane: A Python Library to Build Interactive Reports

Scatterplot,Binned Scatterplot,multi line plot,bubble chart,line charts,bar chart,histogram,boxplot, Pie charts,Line Plot,distplot,Histogram

hvplot A high-level plotting API for the PyData ecosystem built on HoloViews https://hvplot.holoviz.org/

50-charts https://towardsdatascience.com/how-did-i-classify-50-chart-types-by-purpose-a6b0aa5b812d

all in one https://app.learney.me/

Python Tool For Visualizing and Plotting 2D/3D Scientific Data https://analyticsindiamag.com/guide-to-mayavi-a-python-tool-for-visualizing-and-plotting-2d-3d-scientific-data/

patchworklib - combine multiple py charts easily

7 Techniques to Visualize Geospatial Data https://www.kdnuggets.com/2017/10/7-techniques-visualize-geospatial-data.html

data to viz  https://www.data-to-viz.com/ 

Interactive plots directly with pandas https://towardsdatascience.com/get-interactive-plots-directly-with-pandas-13a311ebf426

Top 10 Data Visualization Tools https://www.analyticsvidhya.com/blog/2021/04/top-10-data-visualization-tools/  https://www.xenonstack.com/blog/data-visualization-tools/

https://www.analyticsvidhya.com/blog/2021/03/when-to-use-what-plot-a-beginners-guide-to-select-plots-for-visualization/

https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b

https://datavizproject.com/   https://datavizcatalogue.com/ 

https://attachments.convertkitcdnm.com/232198/ee18f415-1406-4e5c-94f1-49a2c6e3ec4e/Statistics-The-Big-Picture-Poster.pdf

https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b

HiPlot (high dimensional data)-https://github.com/facebookresearch/hiplot https://levelup.gitconnected.com/learn-hiplot-in-6-mins-facebooks-python-library-for-machine-learning-visualizations-330129d558ac

https://towardsdatascience.com/top-6-python-libraries-for-visualization-which-one-to-use-fe43381cd658

https://www.kaggle.com/abhishekvaid19968/data-visualization-using-matplotlib-seaborn-plotly

𝗞𝗲𝗿𝗮𝘀 𝗠𝗼𝗱𝗲𝗹 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿(ann-visualizer)- 𝗽𝗶𝗽𝟯 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗴𝗿𝗮𝗽𝗵𝘃𝗶𝘇

univariate and bivariate and multivariate analysis

model visualization Tensorboard,netron,playground tensorflow,plotly,TensorDash,Dash,Microscope,Lucid

distributions(discerte,continous)

data distributions-normal distribution,Standard Normal Distribution,Student's t-Distribution,Bernoulli Distribution,Binomial Distribution,Poisson Distribution,Uniform Distribution,F Distribution,Covariance and Correlation

Pingouin  statistical package https://pingouin-stats.org/index.html https://www.youtube.com/watch?v=zqi51Wu5qC0 

Types of Statistics  

1.Descriptive

Descriptive statistics :Mean, mode, standard deviation, median ,absolute deviation, kurtosis, skewness

2.Inferential

Types of data

1) Categorical (nomial,ordinal)
 
2) Numerical   (discerte,continous)

random variable(discerte random variable ,continous random variable)

Quantile statistics Q1, Q2, Q3, min, max, range, interquartile range

Central Limit Theorem,Bayes Theorem,Confidence Interval,Hypothesis Testing,z test, t test,f test,Confidence Interval,1 tail test, 2 tail test,chisquare test,anova test,A/B testing

Categorical vs Categorical   Chi-square test,Information gain

Categorical vs Numerical  Student T-test,ANOVA,Logistic regression,Discretize Y (left column)

Numerical  vs  Categorical  Student T-test,ANOVA,Logistic regression,Discretize X (row above)

Numerical vs Numerical  Correlation,Linear Regression,Discretize Y (left column),Discretize X (row above)

upgini Free automated data enrichment library for machine learning https://github.com/upgini/upgini  https://upgini.com/

FeatureSelector https://github.com/WillKoehrsen/feature-selector       feature_engine https://github.com/solegalli/feature_engine

1.Filter methods (Removing Constant feature,Removing Quasi constant feature,Removing Duplication feature,Removing Correlated Features,feature importance,chisquare test,Ttest,ftest,vif,anova test,information gain,F-score,Mutual Information,hypothesis test,information gain,Univariate Selection Methods,SelectKBest,SelectPercentile,Variance threshold,Fisher’s Score,Dispersion ratio Mean Absolute Difference (MAD), constant features elimination, quasi-constant features elimination, duplicate feature elimination,univariate method, mutual information, correlation  etc...),Correlation Coefficient,Variance Threshold ,Mean Absolute Difference (MAD),Dispersion ratio,Variance inflation,factor Condition Index

2.Wrapper methods (recursive feature eliminiation,Recursive feature addition,SelectKbest,boruta,mRMR,forward feature selection,backward feature elimination,Bi-directional selection,exhaustic feature selection,stepwise selection,step forward selection,step backward selection and exhaustive search  etc...)

3.Embedded method (lasso regression,ridge regression,elastic net regression,tree based(Tree-based methods like Random Forest Importance etc...),Feature Selection by Tree importance,Feature selection with decision trees,regression coefficients(logistic,linear coeffiicients),Recursive feature elimination based on importance,Least absolute deviation)

4.Hybrid Method(Recursive Feature Selection,Recursive Feature addition,Recursive feature elimination,Feature Shuffling,Feature performance,Target mean performance,Permutation importance,Population stability index,Target encoding)

unsupervised Feature selection:Principal Component Analysis,Independent Component Analysis,Non-Negative Matrix Factorization,t-distributed Stochastic Neighbor Embedding,Autoencoder

Single-Agent Reinforcement Learning Feature Selection (SARLFS) ,Multi-Agent Reinforcement Learning Feature Selection (MARLFS)

ITMO_FS is a feature selection library https://github.com/ctlab/ITMO_FS

Sparse Features - Removing features,LASSO regularization,features dense(pca,Feature hashing),Using models that are robust to sparse features

5.Feature creation

feature selection  https://medium.com/analytics-vidhya/feature-selection-extended-overview-b58f1d524c1c

mrmr_selection automatic feature selection at scale  https://github.com/smazzanti/mrmr

Feature selector https://github.com/WillKoehrsen/feature-selector

Simulated Annealing https://github.com/kennethleungty/Simulated-Annealing-Feature-Selection

boruta  https://github.com/scikit-learn-contrib/boruta_py https://github.com/Ekeany/Boruta-Shap

DropConstantFeatures  DropDuplicateFeatures    DropCorrelatedFeatures  

step forward feature selection https://www.kdnuggets.com/2018/06/step-forward-feature-selection-python.html

automatic feature selection mrmr https://github.com/smazzanti/mrmr 

Creating New Features Deep Feature Synthesis https://docs.featuretools.com/en/v0.16.0/automated_feature_engineering/afe.html

SequentialFeatureSelector: The popular forward and backward feature selection

Alternative feature selection methods  Feature shuffling,Feature performance,Target mean performance

Automatic Feature Selection  : recursive feature elimination and cross-validation 

Powershap: A Shapley feature selection method https://github.com/predict-idlab/powershap

VarianceThreshold,Chi-squared stats,ANOVA using f_classif,Univariate Linear Regression Tests using f_regression,F-score vs Mutual Information,Mutual Information for discrete value,Mutual Information for continues value,SelectKBest,SelectPercentile,SelectFromModel,Recursive Feature Elimination,Extra Trees model  

4.Feature Importance

   a.ExtraTreesClassifier,ExtraTreesregressor

   b.SelectKBest

   c.Logistic Regression

   d.Random_forest_importance,Permutation Feature Importance
   
   e.decision tree
   
   f.Linear Regression
   
   g.xgboost
   
   h.Pearson correlation
   
   Forward selection,Chi-square,Logit (Logistic Regression model)

5.curse of dimensionality (as dimension increases performance decreases)

6.highly correleated features then can take any 1 feature (multicollinearity)

7.dimension reduction

8.lasso regression to penalise unimportant features

9.VarianceThreshold ,selectkbest

10.model based selection

11.Mutual Information Feature Selection

12.remove features with very low variance (quasi constant feature dropping)

13.Univariate  feature selection

14.importance of feature (random forest importance)

15.feature importance with decision trees

16.PyImpetus

17.drop constant features (variance=0)  , Drop Highly Correlated Features

18.variance inflation factor(vif)

19.Recursive Feature Elimination     RecursiveFeatureAddition

20.exchaustive feature selection

21.Statistical Methods , Hypothesis Testing ,Recursive Feature Elimination

22.Boruta https://github.com/scikit-learn-contrib/boruta_py https://analyticsindiamag.com/hands-on-guide-to-automated-feature-selection-using-boruta/

23.Sequence Feature Selection, SelectFromModel

Missing Value Ratio Analysis,Low Variance Filter,High Correlation Filter,Backward Feature Elimination,Forward Feature Elimination ,SequentialFeatureSelector

PyImpetus https://github.com/atif-hassan/PyImpetus

https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/

Automate your Feature Selection Workflow in one line of Python code https://github.com/AutoViML/featurewiz  https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4

https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/ https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/

https://www.analyticsvidhya.com/blog/2020/10/a-comprehensive-guide-to-feature-selection-using-wrapper-methods-in-python/

https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172

Feature Engineering Tools https://neptune.ai/blog/feature-engineering-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-engineering-tools

https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd

PyRasgo https://github.com/rasgointelligence/PyRasgo https://docs.rasgoml.com/rasgo-docs/?_ga=2.209281745.2123722956.1645542654-525286113.1645542654

Automated Feature Engineering Using Deep Feature Synthesis (DFS)  https://heartbeat.comet.ml/introduction-to-automated-feature-engineering-using-deep-feature-synthesis-dfs-3feb69a7c00b

Automatic Feature Selection in python  https://verstack.readthedocs.io/en/latest/#featureselector
rulefit https://github.com/christophM/rulefit

Featurewiz: Fast way to select the best features in a data
select best features  featurewiz https://github.com/AutoViML/featurewiz
Featuretools: https://github.com/alteryx/featuretools  https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/
AutoFeat: https://github.com/cod3licious/autofeat
TSFresh: https://github.com/blue-yonder/tsfresh
FeatureSelector: https://github.com/WillKoehrsen/feature-selector
unsupervised feature selection technique  https://github.com/atif-hassan/FRUFS
rulefit https://github.com/christophM/rulefit

 Splitting ratio of data deponds on size of dataset available

 Training data,Validation data,Testing data

 Transformers for Tabular Data: TabTransformer https://github.com/lucidrains/tab-transformer-pytorch 

 1.Regression (output feature in continous data form)
 
   linear regression,Multiple Linear Regression,polynomial regression,Exponential Regression,Bayesian Regression,Robust Regression,Huber regressor,support vector regression,Decision Tree Regression,Random Forest Regression,TensorFlow Decision Forests,RANSAC Regression,
   
   least square method,linear-tree,Random Forest Regression, Regularized Greedy Forests,xgboost,ridge(L2 Regularization),lasso(L1 Regularization (more sparse)),elastic, Lars,catboost,gradientboosting,adaboost,Explainable Boosting Machine,Histogram-Based Gradient Boost,Stacked Gradient Boosting Machines,LightBoost,CatBoost, XGBoost,autoxgb,NGBoost,XBNet,Chefboost,GPBoost,Local Cascade Ensemble,Principal Component Regression,huber_regression,ransac_regression,theilsen_regression,Linear spline,Isotonic regression,Bin regression,Cubic spline,Natural cubic splin,Exponential moving average,Quantile Regression,Quantile Random Forests,Quantile GBM
   
   elsatic net,light gbm,ordinary least squares,cart,Stepwise Regression,Multivariate Adaptive Regression Splines ,Generalised Additive Model(learn non-linear feature),tabnet,Linear Tree regression
   
   statsassume Automating Assumption Checks for Regression Models https://github.com/kennethleungty/statsassume
   
   Locally Weighted Linear Regression https://towardsdatascience.com/locally-weighted-linear-regression-in-python-3d324108efbf
   
   TuringBot https://www.youtube.com/watch?v=LyKzKvjyIPo
   
   chefboost is an alternative library for training tree-based models https://github.com/serengil/chefboost
   
   growtrees About Cost-Aware Robust Tree Ensembles for Security Applications https://github.com/surrealyz/growtrees

 2.Classification (output feature in categorical data form)
 
    Binary,Multi-class,Multi-labe
 
    Logistic Regression,K-Nearest Neighbors,Support Vector Machine,Kernel SVM,Naive Bayes,Decision Tree Classification,linear-tree,TensorFlow Decision Forests,
    
    Random Forest Classification,TensorFlow Decision Forests, Regularized Greedy Forests,xgboost,DART booster,autoxgb,LightGBM,adaboost,Gradient Boost,XBNet,catboost,gaussian NB,LGBMClassifier,LinearDiscriminantAnalysis, Extreme Gradient Boosting Machine, Explainable Boosting Machine,fairgbm

    https://mlwhiz.com/blog/2019/11/12/dtsplits/?utm_campaign=the-simple-math-behind-3-decision-tree-splitting-criterions&utm_medium=social_link&utm_source=missinglettr-linkedin
    
    4 Useful techniques avoid overfitting in decision trees https://towardsdatascience.com/4-useful-techniques-that-can-mitigate-overfitting-in-decision-trees-87380098bd3c
    
    Machine Learning – it’s all about assumptions  https://www.kdnuggets.com/2021/02/machine-learning-assumptions.html
    
    GPBoost: A Library To Combine Tree-Boosting With Gaussian Process And Mixed-Effects Models https://analyticsindiamag.com/guide-to-gpboost-a-machine-learning-library-to-combine-tree-boosting/
    
    
    Data and Concept Drift  https://evidentlyai.com/blog/machine-learning-monitoring-data-and-concept-drift

 1.Dimensionality reduction - PCA,ppa,SVD,LDA,som,tsne,openTSNE,plsr,pcr,autoencoders,kernelpca,Latent Semantic Analysis,Factor Analysis,Locality Preserving Projections,Isometric Mapping,Multiple correspondence analysis (MCA),Multiple factor analysis (MFA),Factor analysis of mixed data (FAMD),vae,CompressionVAE,Gaussian Mixture Model,Bayesian Gaussian Mixture Model 
 non-linear data using Kernel PCA, Non-Negative Matrix Factorization(NMF), IsoMap, t-SNE, and UMAP,TDA(Topological Data Analysis)
 
 t-SNE Effectively https://distill.pub/2016/misread-tsne/

 2.Clustering : Centroid-based Model ,Density-based Model ,Distribution-based Model,Connectivity-based model
 
  17 clustering  https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a
  
  https://neptune.ai/blog/clustering-algorithms?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-clustering-algorithms
 
 classix Fast and explainable clustering based on sorting https://github.com/nla-group/classix
 
 https://www.mygreatlearning.com/blog/unsupervised-machine-learning/?highlight=unsupervised%20machine%20learning&utm_source=GLA&utm_medium=Blog&utm_campaign=1-16th%20May
 
 https://scikit-learn.org/stable/modules/clustering.html  https://machinelearningmastery.com/clustering-algorithms-with-python/

 https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a
 
 RFM Segmentation in E-Commerce https://towardsdatascience.com/rfm-segmentation-in-e-commerce-e0209ce8fcf6
 
 kmodes https://www.youtube.com/watch?v=8eATPLDJ0NQ
 
 Agglomerative Hierarchical Clustering Using AGNES Algorithm https://analyticsindiamag.com/perform-agglomerative-hierarchical-clustering-using-agnes-algorithm/
 
 CLARANS Clustering Algorithm https://analyticsindiamag.com/comprehensive-guide-to-clarans-clustering-algorithm/
 
 https://pub.towardsai.net/fully-explained-birch-clustering-for-outliers-with-python-2ad6243f126b
 
 https://www.kdnuggets.com/2020/12/algorithms-explained-k-means-k-medoids-clustering.html 
 
 https://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html
 
 CLASSIX clustering https://github.com/nla-group/classix
 
 K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines   https://www.kdnuggets.com/2021/01/k-means-faster-lower-error-scikit-learn.html#.YAHAAIpnx4A.linkedin

 k-Means Clustering by up to 10x Over Scikit-Learn  https://towardsdatascience.com/how-to-speed-up-your-k-means-clustering-by-up-to-10x-over-scikit-learn-5aec980ebb72
 
 3.Association Rule Learning - support,lift,confidence,leverage,Conviction,aprior,elcat,Fp-growth,Fp-tree construction,FP-Max Algorithm,association_rules,Frequent Itemset Mining,Multi-Relation Association Rules,High-order pattern discovery,K-optimal pattern discovery,Approximate Frequent Itemset,Generalized Association Rules,Quantitative Association Rules,Interval Data Association Rules,Sequential pattern mining,Hypergeometric Networks
 
 Sequential Patterns 
 Generalized Sequential Patterns (GSP)
 Prefix-Projected Sequential Pattern Mining (PrefixSpan)
 Sequential Pattern Discovery using Equivalent Class (SPADE)  
 Frequent Pattern-Projected Sequential Pattern Mining (FreeSpan)
 
 
 interpretable association rule https://analyticsindiamag.com/a-guide-to-interpretable-association-rule-mining-using-pycaret/
 
 4.Market Segmentation
 
 Demographic Segmentation,Geographic segmentation,Firmographic segmentation,Behavioural segmentation,

 4.Recommendation system - Surprise,TensorFlow Recommendation,Recmterics
 
    competitive-recsys  https://github.com/chihming/competitive-recsys
 
     a.collaborative Recommendation system (model based, memory based(item based,user based),hybrid)  user-item interaction matrix
     
     Classification-based collaborative filtering
     
     Model-based collaborative filtering systems(Cluster model,linear regression,Bayesian networks ,latent factor(probabilistic latent,matrix factorization(als,SGD,SVD),neural network,lda))
    
     b.content based Recommendation system 
     
     similarity based(user-user similarity,item-item similarity)
     
     matrix factorization(SVD and SVD++),Popularity-based recommenders
     
     c.utility based Recommendation system 
     
     d.knowledge based Recommendation system 
     
     e.demographic based Recommendation system 
     
     f.hybrid based Recommendation system 
     
     Popularity based Recommendation system (NON-PERSONALIZED )
     
     g.Average Weighted Recommendation
     
     h.using K Nearest Neighbor
     
     i.cosine distance recommender system
     
     item2vec 
     
     j.TensorFlow Recommenders https://www.tensorflow.org/recommenders
     
     recommenders  https://github.com/microsoft/recommenders
     
     Neural Collaborative Filtering for Personalized Ranking  
     
     AutoRec: Rating Prediction with Autoencoders Matrix Factorization
     
     k.suprise baseline model
     
     Context-aware Recommender Systems,Mobile Recommender Systems,Group Recommender Systems,Multi-stakeholder Recommender Systems
     
     l.Tf-Rec https://github.com/Praful932/Tf-Rec
     
     Nvidia Merlin 
     
     m.Deep Learning Recommendation Models https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html
     
     Restricted Boltzmann Machines,Auto-Encoders
     
     TOROS Buffalo https://github.com/kakao/buffalo
     
     recommenders-https://github.com/microsoft/recommenders
     
     LightFM https://making.lyst.com/lightfm/docs/home.html
     
     lkpy Python recommendation toolkit https://github.com/lenskit/lkpy  https://analyticsindiamag.com/how-to-build-recommender-systems-using-lenskit/
     
     torchrec https://github.com/pytorch/torchrec
     
     PyTorch implementations of deep reinforcement learning algorithms and environments https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
     
     recmetrics library of metrics for evaluating recommender systems   https://github.com/statisticianinstilettos/recmetrics
     
     Downsize Recommendation Models By 112 Times https://analyticsindiamag.com/explained-facebooks-novel-method-to-downsize-recommendation-models-by-112-times/
     
     torchrec,Lenskit,RGRecSys,Surprise,Tensorflow Recommenders,NVIDIA-Merlin,Recmetrics,Surprise,DeepCTR,OpenRec,fastFM,LightFM
     
     Session-based RecSys could be done with:Recency-based Weighting (exp.decay),Probabilistic Graphical Models (FPMC, FOSSIL),Convolutional NN (Caser, NextItNet),Recurrent NN (GRU4Rec),Graph NN (SRGNN, GCSAN),Attention(STAMP, NARM, FDSA, SHAN),Transformer(BERT4Rec, Transformer4Rec),Knowledge Graph(KSR, GRU4RecKG, KGCN, KGAT, RippleNet)
     
     https://analyticsindiamag.com/top-open-source-recommender-systems-in-python-for-your-ml-project/
     
     https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8
     
     https://machinelearningmastery.com/recommender-systems-resources/

 1.Stacking models https://www.analyticsvidhya.com/blog/2021/03/advanced-ensemble-learning-technique-stacking-and-its-variants/?

   vecstack https://github.com/vecxoz/vecstack
   
   Cascading Ensembles,Cohorted Ensembles

 2.Bagging models  (Bagging (with the replacement) , Pasting ( without replacement ))

 3.Boosting models
 
 4.Blending
 
 5.Voting (Hard Voting,Soft Voting)
 
 VOTING ENSEMBLE
 
 Simple : Max Voting, Averaging, Weighted Averaging,Simple Average,Rank Averaging,Bayesian Model,Majority Voting
 
 mlens ML-Ensemble – high performance ensemble learning https://github.com/flennerhag/mlens
 
 https://analyticsindiamag.com/do-ensemble-methods-always-work/
 
 Shapley value of players (models) in weighted voting games  https://github.com/benedekrozemberczki/shapley

  2 types a)model free   b)model based
  
  gym-https://github.com/openai/gym           reinforcement learning by using  PyTorch-https://github.com/SforAiDl/genrl

  agent,environment,policy(On-Policy vs Off-Policy),reward function,value function,state,action,episode,actor-critic

  agent apply action to environment get corresponding reward so that it learn environment
  
  How to get started with Reinforcement Learning https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c
  
  1.Q-Learning
  
  2.Deep Q-Learning
  
  3.Deep Convolutional Q-Learning
  
  Deep Deterministic Policy Gradient
  
  4.Twin Delayed DDPG,DQN,Temporal difference
  
  5.A3C  (Actor Critic) ,A2C, Soft Actor Critic (SAC),Adversarial Motion Priors (AMP),Cross-Entropy Method (CEM),Deep Deterministic Policy Gradient (DDPG),Double Deep Q-Network (DDQN),Deep Q-Network (DQN),Proximal Policy Optimization (PPO),Q-learning (Q-learning),Soft Actor-Critic (SAC),State Action Reward State Action (SARSA),Twin-Delayed DDPG (TD3),Trust Region Policy Optimization (TRPO)
  
  6.Advantage weighted actor critic (AWAC). 
  
  7.XCS
  
  8.genetic algorithm,sarsa,natural policy gradient,Policy Gradient Learning
  
  https://simoninithomas.github.io/deep-rl-course/
  
  SARSA,REINFORCE,PPO,DDPG,Ddpg,TD3
  
  AUTORL: AUTOML FOR RL https://www.automl.org/blog-autorl/
  
   Environments-OpenAI Gym, DeepMind Lab, Unity ML-Agents
   
   https://data-flair.training/news/python-libraries-for-reinforcement-learning/
   
   https://analyticsindiamag.com/8-best-free-resources-to-learn-deep-reinforcement-learning-using-tensorflow/   
   
   https://analyticsindiamag.com/top-8-autonomous-driving-open-source-projects-one-must-try-hands-on/
   
   https://analyticsindiamag.com/8-toolkits-for-reinforcement-learning-models-that-make-reasoning-explainability-core-to-ai/
   
   https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses
   
   https://towardsdatascience.com/value-based-methods-in-deep-reinforcement-learning-d40ca1086e1
   
   https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-reinforcement-learning-tutorials-examples-projects-and-courses
   
   TensorForce: A TensorFlow-based Reinforcement Learning Framework https://analyticsindiamag.com/guide-to-tensorforce-a-tensorflow-based-reinforcement-learning-framework/
  
   Decision Transformer: Reinforcement Learning via Sequence Modeling https://github.com/kzl/decision-transformer
   
   Open AI Gym - https://gym.openai.com/
   
   DeepMind’s MuZero  https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?utm_campaign=Learning%20Posts&utm_content=150411901&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323
   
   KerasRL https://github.com/keras-rl/keras-rl
   
   pyqlearning
   
   tensorforce https://tensorforce.readthedocs.io/en/latest/index.html  
   
   Practical_RL https://github.com/yandexdataschool/Practical_RL
   
   rl_coach https://github.com/IntelLabs/coach#installation        MushroomRL https://mushroomrl.readthedocs.io/en/latest/
   
   TFAgents  https://github.com/tensorflow/agents (https://www.tensorflow.org/agents)   https://deepmind.com/blog/article/trfl    
   
   TorchRec https://pytorch.org/blog/introducing-torchrec/ TensorFlow Recommenders https://www.tensorflow.org/recommenders
   
   behaviour trees used in reinforcement learning https://analyticsindiamag.com/how-are-behaviour-trees-used-in-reinforcement-learning/
   
   Automate The Stock Market Using FinRL (Deep Reinforcement Learning Library)  https://analyticsindiamag.com/stock-market-prediction-using-finrl/
   
   Stable Baselines  https://github.com/openai/baselines
   
   https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
   
   https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-the-best-tools-for-reinforcement-learning-in-python

 1.Regression task

 2.Classification task
 
 Tabnet and deep tables for tabular dataset using deep learning

 Best MLOps Tools for Your Computer Vision Project Pipeline https://neptune.ai/blog/best-mlops-tools-for-computer-vision-project?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-mlops-tools-for-computer-vision-project

  mediapipe https://google.github.io/mediapipe/        cv modelhub https://modelplace.ai/
  
  all openmmlab https://github.com/open-mmlab  mmdetection,mmsegmentation,mmediting,mmdetection3d,mmaction2,mmocr,mmpose,etc...
  
  glasses High-quality Neural Networks for Computer Vision  https://github.com/FrancescoSaverioZuppichini/glasses
  
  IceVision https://airctic.com/0.8.0/
  
  Top Computer Vision Google Colab Notebooks- https://www.qblocks.cloud/creators/computer-vision-google-colab-notebooks
  
  for low code object detection (detecto)- https://github.com/alankbi/detecto
  
  CV-pretrained-model-   https://github.com/balavenkatesh3322/CV-pretrained-modelCV-pretrained-model-
  
  Fast Computer Vision Model Building PyTorch Lightning Flash and FiftyOne https://towardsdatascience.com/open-source-tools-for-fast-computer-vision-model-building-b39755aab490
  
  5 Open-Source Facial Recognition   https://medium.com/analytics-vidhya/ways-to-boost-your-computer-vision-projects-by-using-5-open-source-facial-recognition-projects-56668f170cb9
  
  cnn alternative CapsNet https://github.com/XifengGuo/CapsNet-Keras

  EDA for image data data-gradients

 1.Classification of image 
 
   albumentations https://github.com/albumentations-team/albumentations  AugLy https://github.com/facebookresearch/AugLy
 
   create own model,Lenet,Alexnet,DenseNet,MobileNet,ShuffleNet,SqueezeNet,Resenet,GoogleNet,Inception,Vgg16,vgg19,Efficient,EfficientNetV2,EfficientDet,residualnet,Nasnet,STN,nasneta,senet,amoebanetc,DeiT (tiny,small,base),Meta Pseudo Labels,res-mlp-pytorch,MLP-Mixer,vit,DynamicViT, FNet,gMLP models,nfnet
   
   mmclassification https://github.com/open-mmlab/mmclassification
   
   https://theaisummer.com/cnn-architectures/  https://paperswithcode.com/sota/image-classification-on-imagenet 
   
   timm https://pypi.org/project/timm/  https://github.com/rwightman/pytorch-image-models
 
 2.Localization of object in image
 
 3.Object detection and object segmentation 
 
   rcnn,fastrcnn,fastercnn,TensorFlow Object Detection,yolo v1,yolo v2,yolo v3,SlimYOLOv3,yolo v4,PP-YOLO,scaled yolov4,YOLOR,YoloV5,YOLOS,efficinetdet,fast yolo,yolo tiny,yolo lite,yolo tiny++,yolo act++,
   
   maskrcnn,DeepLab-v3-plus,ssd,detectron,detectron2,D2Go,mobilenet,retinanet,R-fcn,Libra_R-CNN,detr facebook,mdetr,pspnet,segnet,U-net,UNet++,Efficient U-Nets, 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁, nnU-Net,v-net,TransUNet, H-DenseUNet, MultiResUNet ,deeplab,globalconvolutionnetwork,fcn,EfficientDet,Vision Transformer,deit,VarifocalNet (VF-Net),DINO,BodyPix,vit,AugFPN,mlsd
   
   PixelLib Simplifying Object Segmentation with PixelLib Library  https://github.com/ayoolaolafenwa/PixelLib

   mmdetection https://github.com/open-mmlab/mmdetection    https://towardsdatascience.com/mmdetection-tutorial-an-end2end-state-of-the-art-object-detection-library-59064deeada3  https://github.com/open-mmlab/mmrotate 
   
   mmdetection3d  https://github.com/open-mmlab/mmdetection3d   mmsegmentation  https://github.com/open-mmlab/mmsegmentation
   
   fewshot https://github.com/open-mmlab/mmfewshot
   
   Zero-Shot Object Detection , annotate dataset https://github.com/microsoft/GLIP
   
   imageai.Detection ObjectDetection       Segmentation models https://github.com/qubvel/segmentation_models
   
   Image-Segmentation-Using-Pixellib
   
   IceVision https://airctic.com/0.8.0/
   
   Image Generation Using TensorFlow Keras https://analyticsindiamag.com/getting-started-image-generation-tensorflow-keras/

   Video Understanding https://towardsdatascience.com/video-understanding-made-simple-with-pytorch-video-and-lightning-flash-c7d65583c37e

   Getting Started With Object Detection Using TensorFlow https://analyticsindiamag.com/object-detection-using-tensorflow/
  
   Instance Segmentation using Mask-RCNN with PixelLib and Python https://www.youtube.com/watch?v=i_-ud01wFhc
   
   MLP   MLP solution for Vision, from Google AI  https://github.com/lucidrains/mlp-mixer-pytorch
   
   MMDetection https://analyticsindiamag.com/guide-to-mmdetection-an-object-detection-python-toolbox/  mediapipe  https://github.com/google/mediapipe
   
   SSL Framework For Object Detection https://analyticsindiamag.com/googles-stac-ssl-framework-for-object-detection/ 
   
   GSDT https://analyticsindiamag.com/gsdt-gnns-for-simultaneous-detection-and-tracking/ 
   
   D2Go Brings Detectron2 To Mobile  https://analyticsindiamag.com/facebooks-d2go-brings-detectron2-to-mobile/
   
   AdelaiDet  open source toolbox for multiple instance-level detection and recognition tasks  https://github.com/aim-uofa/AdelaiDet
   
   3d object detection https://omdena.com/blog/3d-object-detection/?utm_source=linkedin&utm_medium=organic&utm_campaign=blog&utm_term=google-analytics
   
   PyMAF https://analyticsindiamag.com/guide-to-pymaf-pyramidal-mesh-alignment-feedback/
   
   3 kind of object segmentation are available semantic segmentation,instance segmentation,panoptic segmentation
   
   segmentation_models  https://github.com/qubvel/segmentation_models
   
   https://analyticsindiamag.com/guide-to-panoptic-segmentation-a-semantic-instance-segmentation-approach/  https://analyticsindiamag.com/semantic-vs-instance-vs-panoptic-which-image-segmentation-technique-to-choose/
   
   ResNeSt: A Better ResNet with the Same Costs https://analyticsindiamag.com/guide-to-resnest-a-better-resnet-with-the-same-costs/
   
   PAN: Pyramid Attention Network for Semantic Segmentation  https://medium.com/mlearning-ai/review-pan-pyramid-attention-network-for-semantic-segmentation-semantic-segmentation-8d94101ba24a
   
   PyTorch based low code object detection-https://github.com/alankbi/detecto  
   
   https://www.kdnuggets.com/2021/03/extraction-objects-images-videos-5-lines-code.html    
   
   autogluon 
   
   GluonCV  https://medium.com/apache-mxnet/start-fitting-cv-models-like-scikit-learn-with-gluoncv-0-10-931ff910a38 
   
   https://awesomeopensource.com/project/hoya012/deep_learning_object_detection
 
 4.objecttracking  (mean shit and optical flow and kalman filter)
 
   Tracktor++,Trackrcnn,Jde,DeepSORT,FairMOT
   
   mmtracking https://github.com/open-mmlab/mmtracking  https://github.com/open-mmlab/mmflow
   
   mmhuman3d https://github.com/open-mmlab/mmhuman3d
   
   Video Understanding https://github.com/open-mmlab/mmaction2 
 
 5.Deepdream,Neural style transfer, Pose estimation 
 
 generative models https://github.com/open-mmlab/mmgeneration

 Machine Learning for Art https://ml4a.net/#

 Pose estimation by mediapipe library  https://google.github.io/mediapipe/  https://www.youtube.com/watch?v=brwgBf6VB0I
 
 posemodule https://www.youtube.com/watch?v=5kaX3ta398w   Pose Tracking  https://www.youtube.com/watch?v=0JU3kpYytuQ&t=1650s
 
 6.DEEP LEARNING METHODS FOR 2D :OpenPose,DeepPose,AlphaPose,tfpose,MultiPoseNet,AlphaPose,Movenet lighting,VIBE,DeeperCut,Mask RCNN,DeepCut,Convolutional Pose Machines,PoseNet,MoveNet,Adobe’s BodyNet,MoveNet and TensorFlow.js,High-Resolution Net,Blaze pose,Deep Pose,PoseNet
 
 openpose wrnchai  densepose
 
 mmpose https://github.com/open-mmlab/mmpose
 
 Pose Estimation using OpenCV https://www.analyticsvidhya.com/blog/2021/05/pose-estimation-using-opencv/
 
 https://medium.com/beyondminds/an-overview-of-human-pose-estimation-with-deep-learning-d49eb656739b
 
 3D POSE ESTIMATION
 
 3D Image Classification https://keras.io/examples/vision/3D_image_classification/
 
 TensorFlow 2 Object Detection API tutorial https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/
 
 https://blog.paperspace.com/how-to-train-scaled-yolov4-object-detection/
 
 Image DA libraries – Augmentor, Albumentations, ImgAug, AutoAugment, Transforms  https://neptune.ai/blog/data-augmentation-in-python
 
 Simple transformations-Resize,Gray Scale,Normalize,Random Rotation,Center Crop,Random Crop,Gaussian Blur
 
 Position augmentation-Scaling,Cropping,Flipping,Padding,Rotation,Translation,Affine transformation,Kernel filters
 
 Color augmentation-Brightness,Contrast,Saturation,Hue
 
 Deep learning approach-Adverserial training,Neural style transfer,Gan data argumentation
 
 AS-One Run YOLOv7,v6,v5,R,X in under 20 lines of code https://github.com/augmentedstartups/AS-One 
 
 Data augmentation feature space : noise,interpolation   Data Space Character Level : Noise Induction,Rule-based Transformations  Word Level : Noise Induction,Synonym Replacement,Embedding Replacement,Replacement by Language Models  Phrase and Sentence Level : Interpolation,Structure-based Transformation  Document Level:Round-trip Translation,Generative Methods
 
 flipping, rotation, scaling ratio, noise injection, changing contrast, translation, cropping, color jittering,AutoAugment,Fast AutoAugment,Population Based Augmentation,RandAugment
 
 More advanced techniques-Gaussian Noise,Random Blocks,Central Region
 
 albumentations https://github.com/albumentations-team/albumentations https://towardsdatascience.com/getting-started-with-albumentation-winning-deep-learning-image-augmentation-technique-in-pytorch-47aaba0ee3f8
 
 AugLy  A Modern Data Augmentation Library  https://analyticsindiamag.com/complete-guide-to-augly-a-modern-data-augmentation-library/ https://github.com/facebookresearch/AugLy
 
 Data augmentation with tf.data
 
 ImageGenerator  image augmentation  ImageDataGenerator Albumentations  SOLT  Imgaug Augmentor,Albumentations,Imgaug,AutoAugment (DeepAugment)
 
 Augmentor Image augmentation library in Python for machine learning  https://github.com/mdbloice/Augmentor
 
 albumentations  https://github.com/albumentations-team/albumentations 
 
 HiSD: Image-to-Image translation via Hierarchical Style Disentanglement https://analyticsindiamag.com/hisd-python-implementation-of-image-to-image-translation/
 
 Zooming Slow-Mo https://analyticsindiamag.com/guide-to-zooming-slow-mo-one-stage-space-time-video-super-resolution/
 
 Image Augmentation Pipelines with Tensorflow  https://towardsai.net/p/machine-learning/building-complex-image-augmentation-pipelines-with-tensorflow-bed1914278d2
 
 TensorFlow2.0-Examples  https://github.com/YunYang1994/TensorFlow2.0-Examples
 
 unadversarial  https://github.com/microsoft/unadversarial/ https://analyticsindiamag.com/microsoft-research-unadversarial/
 
 CNNs 'see' - FilterVisualizations, Heatmaps,Saliency Maps,saliency_map_guided,Heat Map Visualizations,GradCAM,Class Activation Maps,ZFNet,Lucid,Activation Atlas,Blur Integrated Gradients,concept whitening,Integrated Gradients,SmoothGrad,PytorchRevelio,Feature Visualizer, Guided Gradients, grad_cam,sensitivity_analysis,Captum,Preliminary Methods,Plot Model Architecture,Visualize Filters,Activation based Methods,Maximal Activation,Image Occlusion,Gradient based Methods,Gradient based Class Activation Map

 
 Tools to Design or Visualize Architecture of Neural Network https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
 
 quiver Interactive convnet features visualization for Keras https://github.com/keplr-io/quiver
 
 https://jair-neto.medium.com/a-powerful-method-for-explainability-of-object-detection-algorithms-ace0fe4623e7
 
 https://github.com/utkuozbulak/pytorch-cnn-visualizations  https://microscope.openai.com/models  https://github.com/balavenkatesh3322/CV-pretrained-model
 
 Mediapipe for Python https://google.github.io/mediapipe/
 
 imageai.Detection for Object detection
 
 cnn-raccoon  interactive dashboards for your Convolutional Neural Networks with a single line of code https://github.com/lucko515/cnn-raccoon
 
 deit https://github.com/facebookresearch/deit   https://wandb.ai/thibault-neveu/detr-tensorflow-log/reports/Finetuning-DETR-Object-Detection-with-Transformers-on-Tensorflow-A-step-by-step-tutorial--VmlldzozOTYyNzQ  https://github.com/Visual-Behavior/detr-tensorflow
   
 awesome-computer-vision-models https://github.com/nerox8664/awesome-computer-vision-models
 
 EfficientDet https://github.com/ravi02512/efficientdet-keras
 
 Vision Transformer - Pytorch  https://github.com/lucidrains/vit-pytorch   https://github.com/alohays/awesome-visual-representation-learning-with-transformers 
 
 T2T-ViT https://analyticsindiamag.com/complete-guide-to-t2t-vit-training-vision-transformers-efficiently-with-minimal-data/ https://github.com/yitu-opensource/T2T-ViT
 
 Explainability for Vision Transformers https://github.com/jacobgil/vit-explain
 
 https://keras.io/examples/vision/image_classification_with_vision_transformer/
 
 https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples https://github.com/google-research/vision_transformer 
 
 DeepLab-v3-plus Semantic Segmentation in TensorFlow https://github.com/rishizek/tensorflow-deeplab-v3-plus
 
 DEEP LEARNING METHODS FOR 3D:3D human pose estimation= 2D pose estimation + matching,Integral Human Pose Regression,Towards 3D Human Pose Estimation in the

 Data Augmentation apply to increase size of dataset and performance of model
 
 low code object detection -  detecto  https://github.com/alankbi/detecto 
 
 AutoML  https://github.com/dataloop-ai/AutoML
 
 Object Detection with 10 lines of code-https://www.datasciencecentral.com/profiles/blogs/object-detection-with-10-lines-of-code  https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606
 
 Detecto https://github.com/alankbi/detecto https://medium.com/analytics-vidhya/computer-vision-in-healthcare-detection-of-fractures-3313fe6452fc
 
 OneNet-https://analyticsindiamag.com/onenet/
 
 Norfair https://github.com/tryolabs/norfair
 
 Remo Improves Image Management  https://www.freecodecamp.org/news/manage-computer-vision-datasets-in-python-with-remo/
 
 yolo https://github.com/zzh8829/yolov3-tf2 https://github.com/ultralytics/yolov5 https://github.com/ashishpatel26/Yolov5-King-of-object-Detection  https://github.com/sicara/tf2-yolov4
 
 clip https://github.com/openai/CLIP
 
 bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN  https://analyticsindiamag.com/a-beginners-guide-to-bayesian-cnn/

 1.RNN
 
 2.GRU
 
 3.LSTM (have memory cell,forget gate  etc..)

 Depth Gated RNNs,Clockwork RNNs,RNN Initialized Using Identity Matrix(IRNN)
 
 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸  better than LSTM/GRU https://github.com/ashishpatel26/tcn-keras-Examples
 
 4.Information Discrimination Units (IDU) https://github.com/hjeun/idu

 Train an LSTM Model ~30x Faster Using PyTorch with GPU https://towardsdatascience.com/how-to-train-an-lstm-model-30x-faster-using-pytorch-with-gpu-e6bcd3134c86
 
 all above 3 models have bidirectional also based on problem statement use bidirectional models
 
 Quasi-Recurrent Neural Network  https://github.com/salesforce/pytorch-qrnn
 
 textgenrnn https://github.com/minimaxir/textgenrnn

 gan lab https://poloclub.github.io/ganlab/

 https://neptune.ai/blog/generative-adversarial-networks-gan-applications?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-generative-adversarial-networks-gan-applications
 
 Diffusion Models Beat GANs on Image Synthesis https://paperswithcode.com/paper/diffusion-models-beat-gans-on-image-synthesis?from=n9
 
 MUNIT: Multimodal Unsupervised Image-to-Image Translation (GAN) 
  
 https://jonathan-hui.medium.com/gan-gan-series-2d279f906e7b

 generative adversarial transformers https://github.com/dorarad/gansformer
 
 LipGAN https://github.com/Rudrabha/LipGAN  Wav2Lip https://github.com/Rudrabha/Wav2Lip
 
 BigGAN https://analyticsindiamag.com/hands-on-guide-to-biggan-with-python-code/

 Cycle gan,Big GAN Style GAN,Dcgan,cGAN,SRGAN,InfoGAN,stargan,attan gan,stylegan,,PixelRNN,StackGAN,DiscoGAN,lsGAN,Conditional GAN(Pix2Pix),Progressive GANs( produces higher resolution images,Image-to-Image Translation),Face Inpainting,Super-resolution,Progressive Growing GAN,Instance-Conditioned GAN,Wasserstein GAN(improve image generation),ChromaGan,GANsformers,Conditional GAN and Unconditional GAN,Least Square GAN,Auxilary Classifier GAN,Dual Video Discriminator GAN,SRGAN,StackGAN,CycleGAN,WGAN

 diffusion https://github.com/openai/guided-diffusion
 
 https://www.analyticsvidhya.com/blog/2021/05/progressive-growing-gan-progan/
 
 5 Alternatives To Deep Nostalgia https://analyticsindiamag.com/top-5-alternatives-to-deep-nostalgia/
 
 MixNMatch https://github.com/Yuheng-Li/MixNMatch
 
 Quantum GAN  https://analyticsindiamag.com/now-gans-are-being-used-for-drug-discovery-complete-guide-to-quantum-gan-with-python-code/
 
 https://analyticsindiamag.com/guide-to-differentiable-augmentation-for-data-efficient-gan-training/  https://analyticsindiamag.com/hands-on-python-guide-to-style-based-age-manipulation-sam-technique/
 
 Imaginaire https://analyticsindiamag.com/guide-to-nvidia-imaginaire-gan-library-in-python/
 
 Disentanglement https://analyticsindiamag.com/what-is-face-identity-disentanglement-and-how-it-outperformed-gans/
 
 StyleFlow https://github.com/RameenAbdal/StyleFlow 
 
 https://github.com/hindupuravinash/the-gan-zoo  https://analyticsindiamag.com/top-10-tools-for-generative-adversarial-networks/

  1.sparse Autoencoder
  
  2.denoising Autoencoder
  
  3.Contractive Autoencoder
  
  4.stacked Autoencoder
  
  5.deep Autoencoder
  
  6.variational autoencoder
 
  7.convolutional   autoencoder
  
 Beta Variational Autoencoder,VAE with Linear Normalizing Flows ,VAE with Inverse Autoregressive Flows ,Disentangled Beta Variational Autoencoder,Disentangling by Factorising (FactorVAE),Beta-TC-VAE (BetaTCVAE),Importance Weighted Autoencoder (IWAE),VAE with perceptual metric similarity,Wasserstein Autoencoder (WAE),Info Variational Autoencoder,VAMP Autoencoder (VAMP),Hyperspherical VAE (SVAE),Adversarial Autoencoder (Adversarial_AE),Variational Autoencoder GAN (VAEGAN) ,Vector Quantized VAE (VQVAE),Hamiltonian VAE (HVAE),Regularized AE with L2 decoder param (RAE_L2),Regularized AE with gradient penalty (RAE_GP),Riemannian Hamiltonian VAE (RHVAE)
  
  https://github.com/zc8340311/RobustAutoencoder
  
  Applications of AutoEncoders,Dimensionality reduction,Anomaly detection,Image denoising,Image compression,Image generation

 regex,PRegEx  (https://github.com/manoss96/pregex)

 Clean data(removing stopwords depond on problem ,lowering data,tokenization,postagging,stemmimg or lemmatization depond on problem,skipgram,n-gram,chunking)
 
 clean text https://github.com/jfilter/clean-text
 
 Cleaning and Pre-processing textual data with NeatText library   Automated NLP Pre-Processing using Data-Purifier Library https://github.com/Elysian01/Data-Purifier  
 
 Nltk,spacy,genism,textblob,inltk,Indic NLP,StanfordNLP,Pattern,stanza,OpenNLP,polygot,corenlp,polyglot,PyDictionary,Huggiing face,spark nlp,allen nlp,rasa nlu,Megatron,texthero,Flair,textacy,finetune,gluon-nlp,VnCoreNLP,fasttext,Langid,PyCLD3,Guesslang,Parrot  libraries
 
 keyword library Rake_NLTK, Spacy, Textrank, Word cloud, KeyBert, Yake, MonkeyLearn API and Textrazor API.
 
 jiant is an NLP toolkit https://github.com/nyu-mll/jiant
 
 clean-text  https://github.com/jfilter/clean-text https://www.youtube.com/watch?v=i2TjAgga1YU
 
 indicnlp https://indicnlp.ai4bharat.org/samanantar/
 
 Augmenting Data For NLP Tasks  https://towardsdatascience.com/tips-tricks-augmenting-data-for-nlp-tasks-983e33ad55a7 https://amitness.com/2020/05/data-augmentation-for-nlp/ https://github.com/makcedward/nlpaug https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28
 
 NLP Data Augmenting  https://lnkd.in/eHa2cH6
 
 Text Data Augmentation in Natural Language Processing with Texattack https://www.analyticsvidhya.com/blog/2022/02/text-data-augmentation-in-natural-language-processing-with-texattack/
 
 Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing https://www.tagalog.ai/tagalog/
 
 https://github.com/jasonwei20/eda_nlp  https://github.com/dsfsi/textaugment https://github.com/QData/TextAttack https://github.com/makcedward/nlpaug
 
 nlp_profiler https://analyticsindiamag.com/complete-guide-on-nlp-profiler-python-tool-for-profiling-of-textual-dataset/
 
 doccano text annotation tool https://github.com/doccano/doccano https://www.youtube.com/watch?v=vT-GE_jssPk https://github.com/doccano/auto-labeling-pipeline   https://github.com/doccano/doccano-client https://doccano.herokuapp.com/
 
 Data augmentation for NLP-https://github.com/makcedward/nlpaug
 
 Data Augmentation library for text nlpaug https://towardsdatascience.com/data-augmentation-library-for-text-9661736b13ff
 
 doccano,Parrot_Paraphraser,NLPAug,AugLy
 
 detext-https://github.com/linkedin/detext
 
 nlpaug-https://github.com/makcedward/nlpaug  augmenty https://github.com/KennethEnevoldsen/augmenty
 
 NLP-progress -https://github.com/sebastianruder/NLP-progress
 
 Super Duper NLP Repo- https://notebooks.quantumstat.com/
 
 Multilingual Representations for Indian Languages https://tfhub.dev/google/MuRIL/1
 
 Natural Language Processing 365- https://ryanong.co.uk/natural-language-processing-365/
 
 1 line for hundreds of NLP models and algorithms- https://github.com/JohnSnowLabs/nlu
 
 simpletransformers
 
 beautiful Wordclouds in Python  https://towardsdatascience.com/how-to-easily-make-beautiful-wordclouds-in-python-55789102f6f5
 
 Automate your Text Processing workflow in a single line of Python Code https://towardsdatascience.com/automate-your-text-processing-workflow-in-a-single-line-of-python-code-e276755e45de
 
 quantumstat  https://index.quantumstat.com/
 
 Dynaboard: Moving beyond accuracy to holistic model evaluation in NLP https://ai.facebook.com/blog/dynaboard-moving-beyond-accuracy-to-holistic-model-evaluation-in-nlp/
 
 gobbli for interactive NLP  https://medium.com/rti-cds/using-gobbli-for-interactive-nlp-f60feb41e5cb
 
 AutoReg  Regex of string in Python https://github.com/SusmitPanda/AutoReg
 
 Negation Handling Increasing Accuracy of Sentiment Classification
 
 NLU,NLG,NER,text summarization,Sentiment Analysis,Text Classifications,machine translation,chat bot,Text Generation,Speech Recognition

 Case Normalization,regex,Lowercasing,sent_tokenize,Tokenization,Remove Punctuations,Removing Stopwords,Removing Unicode,Removal of(Noise, URLs, Hashtag and User-mentions Hashtag),Replacing Emoticons,Removing Number,Correction of Spelling mistakes,Expanding Contractions,Removing Emojis,Convert Emoji,Remove Emoticon,Removing URLs,Hashtags,text normalization,Noise Removal,Punctuation,Spell Correction,Stemming or Lemmatization 
  
 1.One-hot-encoding,Index-based Encoding,Term Frequency,bag of words ,Bag of N-grams Model,Binary Term Frequency,(L1) Normalized Term Frequency,(L2) Normalized TF-IDF
 
 2.Tfidf ,Weighted Class TF-IDF,tfidf + CHI²,HashingVectorizer
 
 3.wordembedding :  Use a pre-trained model , Self-Trained model
    
    a.using pretrained model 
      
      i)word2vec( cbow,skipgram) ,AvgWord2vec
      
      ii)glove        https://medium.com/spark-nlp/1-line-to-glove-word-embeddings-with-nlu-in-python-baed152fff4d
      
      iii)fast text
      
      iv)MetaVec    
    
    b.creating own embedding  (use when have huge data)
    
      i)word2vec library
      
      ii)keras embedding 
      
  elmo (store semantic of word)
  
  Context-independent 
  Context-independent without machine learning Bag-of-words,TF-IDF
  Context-independent with machine learning Word2vec (Bag of Words (CBoW) and Skip-Gram )  GloVe  fastText

  Context-dependent 
  Context-dependent and RNN based(elmo,cove)
  Context-dependent and transformer-based (BERT ,xlm,RoBERTa,ALBERT)
  
  contextual embeddings: AllenNLP ELMo, OpenAI’s GPT,GPT1,GPT2,GPT3, and Google’s BERT
  
  Fast_Sentence_Embeddings Compute Sentence Embeddings Fast  https://github.com/oborchers/Fast_Sentence_Embeddings
  
  Universal Embeddings, Contextual Embeddings (Transformers),BERT Embeddings,Sentence Transformers,Sentence Vectors,Sentence Embedding
  
  Transformer based embedding 
  
3 b Tokenizer nlp(texs_to_sequences )
    
 4.Document embedding-Doc2vec
  
 5.sentence embedding

   sense2vec,SENT2VEC,Universal sentence encoder,Sentence Transformers
   
 Top2Vec 
 
 Topic Modelling https://towardsdatascience.com/april-edition-adventures-in-topic-modelling-7ee9081a48a0
 
 Doc2Vec  Distributed memory model , Distributed bag of word,Node2Vec,Top2Vec,Doc2Vec,Item2Vec
 
 Elmo, BERT,Universal Sentence Encoder, Sentence Transformers
 
 6.using rnn,lstm,gru
 
   Conventional RNN,Deep Transition RNN,DT(S)-RNN,DOT-RNN,Stacked RNN
 
   for above 3 models have bidirectional also
   
   textgenrnn generate text  https://github.com/minimaxir/textgenrnn
 
 7.Encoder and Decoder(sequence to sequence), ProphetNet(new pretrained seq2seq model)
  
 8.attention 
 
   self attention,Global Attention,Multi-Head Attention,Local Attention (monotonic,predictive)    https://github.com/uzaymacar/attention-mechanisms
   
   Seq2seq with Attention,Self-attentionm,Multi-head Attention
 
 9.Transformer (big breakthrough in NLP) - http://jalammar.github.io/illustrated-transformer/  

    Build a Transformer in JAX from scratch https://theaisummer.com/jax-transformer/
    
    Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing  https://github.com/nlp-uoregon/trankit
 
    FastFormers  https://medium.com/ai-in-plain-english/fastformers-233x-faster-transformers-inference-on-cpu-4c0b7a720e1
 
    Shrinking Transformers (reduce size)  1.quantization,distillation,pruning,
    
    Reformer,Performers,vision transformer
    
    Reformer: The Efficient Transformer
    
    Longformer: The Long-Document Transformer
    
    ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
    
    DeLighT: Deep and Light-weight Transformer  https://analyticsindiamag.com/complete-guide-to-delight-deep-and-light-weight-transformer/
    
    https://github.com/balavenkatesh3322/NLP-pretrained-model
    
    Tree-Transformer https://github.com/yaushian/Tree-Transformer
    
    Scalable Transformer-based Model  https://analyticsindiamag.com/guide-to-perceiver-a-scalable-transformer-based-model/
    
    Transformers Interpret https://towardsdatascience.com/introducing-transformers-interpret-explainable-ai-for-transformers-890a403a9470 https://github.com/cdpierse/transformers-interpret  https://analyticsindiamag.com/hands-on-guide-to-the-evolved-transformer-on-neural-machine-translation/
    
    Novel Interpretable Transformer https://github.com/hila-chefer/Transformer-Explainability  https://analyticsindiamag.com/compute-relevancy-of-transformer-networks-via-novel-interpretable-transformer/
    
    https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html#.YE7gRy9s-LA.linkedin
    
    mBART-50 https://www.youtube.com/watch?v=fxZtz0LPJLE&feature=youtu.be
    
    Few-shot classification with SetFit and a custom dataset  https://rubrix.readthedocs.io/en/docs-setfit_tutorial/tutorials/few-shot-classification-with-setfit.html
  
 10.BERT,Packed BERT,BART,DynaBERT,SBERT,ConvBert,Quantized MobileBERT,ALBERT,ELECTRA,ARBERT,MARBERTElectra,Transformer-XL,Longformer,Reformer,DistilBERT,ELMo,ROBERTA,XLNet,XLM-RoBERTa,DeBERTa,T5,fastT5, CodeT5,mT5,ByT5,simpleT5,byt5,OnnxT5,FastT5,Linformer,DISTILBERT,GPT,GPT2,GPT3,gpt-neo,gpt-neox,GPT-J,aitextgen,PRADO,PET,BORT,MuRIL,Multitask Unified Model,aitextgen,AI21's 'Jurassic' language model,Turing NLG,Wu Dao 2.0,PanGu-Alpha,Gopher,Megatron model

    https://neptune.ai/blog/bert-and-the-transformer-architecture-reshaping-the-ai-landscape
    
    gpt3 https://www.producthunt.com/posts/100-resources-on-gpt-3 
    
    Graph4NLP  https://dlg4nlp.github.io/index.html

    Feedback Transformers from Facebook AI https://towardsdatascience.com/feedback-transformers-from-facebook-ai-221c5dd09e3f

    DETR  https://analyticsindiamag.com/how-to-detect-objects-with-detection-transformers/  https://github.com/dddzg/up-detr
    
    DeiT https://analyticsindiamag.com/introducing-deit-data-efficient-image-transformers/ https://github.com/facebookresearch/deit
    
    80+ NLP tasks  https://medium.com/innerdoc/80-natural-language-processing-tasks-described-c777bc4974b3
    
    Text-to-Image  https://www.datasciencecentral.com/profiles/blogs/summarizing-popular-text-to-image-synthesis-methods-with-python
    
    NLP: Pre-trained Sentiment Analysis https://medium.com/@b.terryjack/nlp-pre-trained-sentiment-analysis-1eb52a9d742c
    
    Awesome-NLP-Resources -https://github.com/Robofied/Awesome-NLP-Resources  https://shivanandroy.com/awesome-nlp-resources/   https://github.com/keon/awesome-nlp
    
    10 Popular Keyword Extraction Algorithms in Natural Language Processing https://prakhar-mishra.medium.com/10-popular-keyword-extraction-algorithms-in-natural-language-processing-8975ada5750c
    
    https://medium.com/@jatinmandav3/opinion-mining-sometimes-known-as-sentiment-analysis-or-emotion-ai-refers-to-the-use-of-natural-874f369194c0#:~:text=fastText%20is%20a%20library%20for,pretrained%20models%20for%20294%20languages
 
    https://analyticsindiamag.com/top-ten-bert-alternatives-for-nlu-projects/  https://towardsdatascience.com/from-pre-trained-word-embeddings-to-pre-trained-language-models-focus-on-bert-343815627598
    
    
    GPT2 generated Indian Food Recipes https://www.kaggle.com/nulldata/gpt2-generated-indian-food-recipes
 
    http://jalammar.github.io/    http://jalammar.github.io/illustrated-bert/   http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
    
    https://jalammar.github.io/explaining-transformers/    https://jalammar.github.io/hidden-states/
    
    https://www.kdnuggets.com/2019/09/bert-roberta-distilbert-xlnet-one-use.html
    
 11.Speech  (Braina,Dragon Speech Recognition Solutions ,Winscribe,Gboard,Windows 10 Speech Recognition,Otter,Speechnotes,tts,OpenSpeech,FRILL,Vakyansh)
 
    audio data augmentation https://github.com/iver56/audiomentations
   
    speech to text   
    
    text to speech  https://towardsdatascience.com/text-to-speech-one-small-step-by-mankind-to-create-lifelike-robots-54e19f843b21
    
    Acoustic model,Speaker diarisation,apis,apiai,assemblyai,google-cloud-speech,pocketsphinx,SpeechRecognition,watson-developer-cloud,wit,Coqui TTS,Mozilla TTS, OpenTTS,ESPNet,PaddleSpeech,Wav2Vec, Whisper, DeepSpeech,Eesen,TensorFlowASR,Vosk,CMUSphinx,Pocketsphinx,KoNLPy,Madmom,HTK,Pysptk
    
    Microsoft IceCAPS is an Open Source Framework for Conversational Modeling https://pub.towardsai.net/microsoft-icecaps-is-an-open-source-framework-for-conversational-modeling-4f78492ca685
    
    State-of-the-art Approaches to Building Open-Domain Conversational Agents https://www.topbots.com/conversational-ai-open-domain-chatbots/?utm_source=twitter&utm_medium=company_post&utm_campaign=conversational_open_domain_chatbots
    
    LaMDA: our breakthrough conversation technology https://www.blog.google/technology/ai/lamda
    
    assemblyai https://www.assemblyai.com/
    
    bark  https://github.com/suno-ai/bark
    
    SpeechBrain A PyTorch Powered Speech Toolkit https://speechbrain.github.io/  https://github.com/speechbrain/speechbrain
    
    Wav2vec-U learns to recognize #speech from unlabeled data https://venturebeat.com/2021/05/21/facebook-wav2vec-u-learns-to-recognize-speech-from-unlabeled-data/?utm_source=dlvr.it&utm_medium=linkedin
    
    Wav2Vec2 https://huggingface.co/transformers/model_doc/wav2vec2.html https://www.youtube.com/watch?v=dJAoK5zK36M&feature=youtu.be
    
    SincNet is a neural architecture for efficiently processing raw audio samples https://github.com/mravanelli/SincNet
    
    HuggingFace Transformers ASR  https://github.com/dennisbakhuis/Ecare_Brunch_ASR
    
    English speech recognition https://github.com/openai/whisper
    
    https://github.com/balavenkatesh3322/audio-pretrained-model
    
 SpeechRecognition  ASR2K: Speech Recognition https://github.com/xinjli/asr2k
 
 audiomentations  Python library for audio data augmentation  https://github.com/iver56/audiomentations
 
 googletrans (google Translator)   https://pypi.org/project/googletrans/
 
 lang-identification   Google Compact Language Detector,FastText
 
 𝗴𝗧𝗧𝗦 for text to speech conversion , 𝘀𝗽𝗲𝗲𝗰𝗵_𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻,TTS
 
 Python/Pytorch app for easily synthesising human voices https://github.com/BenAAndrew/Voice-Cloning-App
 
 Speech-Transformer-tf2.0 https://github.com/xingchensong/Speech-Transformer-tf2.0
 
 The Super Duper NLP Repo  https://notebooks.quantumstat.com/
 
 ecco https://github.com/jalammar/ecco  https://www.eccox.io/   https://www.youtube.com/watch?v=rHrItfNeuh0&feature=youtu.be 
 
 Language Interpretability Tool (LIT) is an open-source platform for visualization and understanding of NLP models https://pair-code.github.io/lit/
 
 Language Interpretability Tool https://github.com/pair-code/lit https://ai.googleblog.com/2020/11/the-language-interpretability-tool-lit.html
 
 autonlp  https://analyticsindiamag.com/hands-on-guide-to-using-autonlp-for-automating-sentiment-analysis/
    
 https://medium.com/towards-artificial-intelligence/natural-language-processing-nlp-with-python-tutorial-for-beginners-1f54e610a1a0
 
 https://pakodas.substack.com/p/neural-search-on-indian-languages     
 
 https://www.linkedin.com/pulse/natural-language-processing-2020-year-review-ivan-bilan/?trackingId=CYfd1ZyLStu6x09tjVIoGw%3D%3D
 
 ConvBert https://github.com/yitu-opensource/ConvBert
 
 Python interface for building, loading, and using GloVe vectors https://github.com/Lguyogiro/pyglove
 
 SentenceTransformers  https://www.sbert.net/
 
 Reformer – The Efficient Transformer  https://analyticsindiamag.com/hands-on-guide-to-reformer-the-efficient-transformer/
 
 Funnel-Transformer https://github.com/laiguokun/Funnel-Transformer
 
 CLIP – Connecting Text To Images  https://analyticsindiamag.com/hands-on-guide-to-openais-clip-connecting-text-to-images/ 
 
 Topic Modeling in One Line with Top2Vec https://towardsdatascience.com/topic-modeling-in-one-line-with-top2vec-a413991aa0ef
 
 MT5-https://venturebeat.com/2020/10/26/google-open-sources-mt5-a-multilingual-model-trained-on-over-101-languages/?utm_content=144321587&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012
 
 VADER does not require any training data https://pypi.org/project/vaderSentiment/  https://analyticsindiamag.com/sentiment-analysis-made-easy-using-vader/
 
 APPLICATIONS OF MACHINE TRANSLATIO-Text-to-text,Text-to-speech,Speech-to-text,Speech-to-speech,Image (of words)-to-text
 
 Google-GNMT (Tensorflow),Facebook-fairseq (Torch),Amazon-Sockeye (MXNet),NEMATUS (Theano),THUMT (Theano),OpenNMT (PyTorch),StanfordNMT (Matlab),DyNet-lamtram(CMU),EUREKA(MangoNMT
 
 awesome-gpt3 https://github.com/elyase/awesome-gpt3
 
 Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym
 
 https://analyticsindiamag.com/best-nlp-based-seo-tools-for-2021/  https://towardsdatascience.com/5-nlp-models-that-you-need-to-know-about-754594a3225b
 
 https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html   https://analyticsindiamag.com/flair-hands-on-guide-to-robust-nlp-framework-built-upon-pytorch/
 
 https://medium.com/modern-nlp/nlp-metablog-a-blog-of-blogs-693e3a8f1e0c
 
 summarization https://github.com/hyunwoongko/summarizers ctrl-sum  https://github.com/salesforce/ctrl-sum

  here data split is different (train,test,validate)
  
  here handling missing data different 
  
  Time Series Decomposition In Python  trend, seasonality,Cyclical and noise https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2
  
  Removing trend  Differencing,Least square trends removal
  
  Converting Non- stationary into stationary  Detrending,Differencing,Transformation
  
  Time Series Decomposition  log,box-cox  transformation,moving average
  
  Removing seasonality Seasonal differencing,Seasonal means,Method of moving averages
  
  generally used  to impute data in Time Series
  
  1.ffill
  
  2.bfill 
  
  3.do mean of previous or future x samples and impute
  
  4.take previous season value and impute (data with trend)
  
  5.mean,mode,median,random sample imputation (data without trend and without seasonality)
  
  6.linear interpolation(data with trend and without seasonality)
  
  7.seasonal +interpolation(data with trend and with seasonality)
  
  here model selection deponds on different property of data like stationary,trend,seasonality,cyclic 
  
  Anomaly Detection using Isolation Forest,AutoEncoders
  
  Granger Causality Statistical Test use for variable usable for forecast 
  
  adfuller test  for  Stationarity        Non Stationary Statistical Test - KPSS and ADF  ACF, PACF, decomposition, ADF test
  
  Handling Data with Regular Gaps using Facebook Prophet
  
  models 
  
  1.AR,VR, VAR, MA, ARMA, ARIMA, auto arima(pmd arima) ,seasonal arima(SARIMA),SARIMAX models
  
  2.Autoregressive,Vector Autoregression,Vector Autoregression Moving-Average,Vector Autoregression Moving-Average with Exogenous Regressors
  
  3.Moving average,Exponential Moving average,Exponential Smoothing,Simple average, Holt’s linear trend method, Holt’s Winter seasonal method,DeepAR,N-BEATS
  
  11 Classical Time Series Forecasting Methods in Python https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
  
  4.XGBoost,Lstm(neural network),DeepAR ( An RNN Algorithm)
  
  5.GARCH
  
  atspy	Automated time-series models
  
  6.Navie forecasts
  
  7.Smoothing (moving average,exponential smoothing)
  
  8.Facebook prophet (note:expceted date column as ds and target column as y) https://thecleverprogrammer.com/2020/12/14/facebook-prophet-model-with-python/
  
  NeuralProphet Model- https://ourownstory.github.io/neural_prophet/model-overview/ https://thecleverprogrammer.com/2021/01/28/neuralprophet-model-with-python/
  
  bulbea Deep Learning based Python Library for Stock Market Prediction and Modelling https://github.com/achillesrasquinha/bulbea
  
  PyTorch Forecasting enables deep learning models for time-series forecasting  pytorch-ts https://github.com/zalandoresearch/pytorch-ts
  
  ETSformer-pytorch https://github.com/lucidrains/ETSformer-pytorch
  
  Transformer Networks to build a Forecasting model https://towardsdatascience.com/how-to-use-transformer-networks-to-build-a-forecasting-model-297f9270e630
  
  Temporal Fusion Transformer (By Google)
  
  hmmlearn https://github.com/ushareng/StockPricePredictionUsingHMM_Byte/blob/master/StockPricePredictionUsingHMM.ipynb
  
  pyramid-arima https://github.com/tgsmith61591/pyramid
  
  pyflux: time series library: https://github.com/RJT1990/pyflux
  
  orbit https://eng.uber.com/orbit/ 
  
  greykite A flexible, intuitive and fast forecasting library https://github.com/linkedin/greykite https://www.analyticsvidhya.com/blog/2021/05/greykite-time-series-forecasting-in-python/
  
  Silverkite
  
  LinkedIn open-sources Greykite, a library for time series forecasting  https://github.com/linkedin/greykite/stargazers
  
  stumpy https://github.com/TDAmeritrade/stumpy
  
  Giotto-Time Time-Series Forecasting Python Library https://github.com/giotto-ai/giotto-time https://analyticsindiamag.com/guide-to-giotto-time-a-time-series-forecasting-python-library/
  
  Informer (for Long Sequence Time-Series Forecasting) https://analyticsindiamag.com/informer/ 
  
  tfcausalimpact https://github.com/WillianFuks/tfcausalimpact
  
  deepar is global model https://www.youtube.com/watch?v=xcbj0RE3kfI&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=14
  
  pmdarima for Auto ARIMA
  
  GluonTS https://github.com/awslabs/gluon-ts
  
  sktime — a unified time-series framework for Scikit-Learn
  
  tsfresh — a magical library for feature extraction in time-series datasets
  
  ThymeBoost Forecasting with Gradient Boosted Time Series Decomposition https://github.com/tblume1992/ThymeBoost

  darts A python library for easy manipulation and forecasting of time series  https://github.com/unit8co/darts

  Kats https://github.com/facebookresearch/Kats

  Time Series Outlier Detection with ThymeBoost

  AtsPy: Automated Time Series Models in Python https://github.com/firmai/atspy

  Merlion: A Machine Learning Framework for Time Series Intelligence https://github.com/salesforce/Merlion
  
  stumpy powerful and scalable Python library for modern time series analysis https://github.com/TDAmeritrade/stumpy 

  mlforecast Scalable machine learning based time series forecasting https://github.com/Nixtla/mlforecast
  
  statsforecast  Lightning ⚡️ fast forecasting with statistical and econometric models https://github.com/Nixtla/statsforecast
 
  9.Holts winter,Holts linear trend
  
  10.Auto_Timeseries by auto-ts    https://www.youtube.com/watch?v=URUiVD37fns&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=24 tell best model for data
  
  AutoTS-https://analyticsindiamag.com/hands-on-guide-to-autots-effective-model-selection-for-multiple-time-series/  https://github.com/AutoViML/Auto_TS
  
  Automated Time Series Forecasting  https://github.com/winedarksea/AutoTS  ,  No-Code AI Forecasting Platform https://datafloat.ai/
  
  AutoML for time series: advanced approaches with FEDOT framework  https://towardsdatascience.com/automl-for-time-series-advanced-approaches-with-fedot-framework-4f9d8ea3382c

  AutoML for time series: definitely a good idea https://towardsdatascience.com/automl-for-time-series-definitely-a-good-idea-c51d39b2b3f
  
  AutoReg  Regex of string in Python https://github.com/SusmitPanda/AutoReg
  
  pytsal  low-code open-source python framework for Time Series analysis,visualization,forecasting along with AutoTS  https://github.com/KrishnanSG/pytsal
  
  Automated Time Series Forecasting https://github.com/winedarksea/AutoTS
  
  Forecasting with H2O AutoML https://github.com/business-science/modeltime.h2o/
  
  Forecasting Stock Prices Using Stocker  https://medium.com/mlearning-ai/forecasting-stock-prices-using-stocker-7d2ac15966f5
  
  MiniRocket: Fast(er) and Accurate Time Series Classification https://towardsdatascience.com/minirocket-fast-er-and-accurate-time-series-classification-cdacca2dcbfa
  
  modeltime https://github.com/business-science/modeltime
  
  GluonTS , PytorchTS   https://analyticsindiamag.com/gluonts-pytorchts-for-time-series-forecasting/
  
  stocker https://medium.datadriveninvestor.com/forecasting-stock-prices-using-stocker-66503c26307a
  
  11.Temporal Convolutional Neural
  
  12.Atspy For Automating The Time-Series Forecasting-https://analyticsindiamag.com/hands-on-guide-to-atspy-for-automating-the-time-series-forecasting/
  
  13.Darts-https://analyticsindiamag.com/hands-on-guide-to-darts-a-python-tool-for-time-series-forecasting/
  
  14.Bayesian Neural Network , TsEuler
  
  15.PyFlux (easy way to compare different models)-https://analyticsindiamag.com/pyflux-guide-python-library-for-time-series-analysis-and-prediction/
  
  16.Orbit , DeepAR ,NeuralProphet(https://github.com/ourownstory/neural_prophet    https://ourownstory.github.io/neural_prophet/model-overview/)

  IBM’s AutoAI automates time series forecasting https://www.ibm.com/blogs/research/2021/03/autoai-time-series/?utm_campaign=Learning%20Posts&utm_content=159454790&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323
  
  Kats all in 1  time seres data https://github.com/facebookresearch/kats  https://facebookresearch.github.io/Kats/

  orbit https://analyticsindiamag.com/hands-on-guide-to-orbit-ubers-python-framework-for-bayesian-forecasting-inference/ https://github.com/uber/orbit
  
  best article-https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/
  
  TimeSynth https://github.com/TimeSynth/TimeSynth  https://analyticsindiamag.com/guide-to-timesynth-a-python-library-for-synthetic-time-series-generation/
  
  time series visualization tool https://plotjuggler.io/
  
  Time Series Anomaly Detection using Generative Adversarial Networks(TadGAN) https://analyticsindiamag.com/hands-on-guide-to-tadgan-with-python-codes/
  
  fastquant — Backtest and optimize your trading strategies with only 3 lines of code  https://github.com/enzoampil/fastquant 

  pytorch-forecasting  https://github.com/jdb78/pytorch-forecasting  https://analyticsindiamag.com/guide-to-pytorch-time-series-forecasting/ 
  
  https://pytorch-forecasting.readthedocs.io/en/latest/  https://pytorch-forecasting.readthedocs.io/en/latest/tutorials/ar.html
  
  Complex Exponential Smoothing (CES) which can handle both stationary and non-stationary processes and model a wide spectum of level and trend time-series.  https://github.com/Nixtla/statsforecast/tree/main/experiments/ces
  
  sktime-https://github.com/alan-turing-institute/sktime  https://analyticsindiamag.com/sktime-library/
  
  autocast https://github.com/andyzoujm/autocast
  
  tsfresh – a magical library for feature extraction in time-series datasets.
  
  atspy  https://github.com/firmai/atspy
  
  tcn https://towardsdatascience.com/farewell-rnns-welcome-tcns-dd76674707c8
  
  Pastas https://analyticsindiamag.com/guide-to-pastas-a-python-framework-for-hydrogeological-time-series-analysis/ https://github.com/pastas/pastas
  
  stockDL https://github.com/ashishpapanai/stockDL
  
  decompsition https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2
  
  Bayesian Diffusion Modeling  https://www.topbots.com/bayesian-diffusion-modeling/
  
  Top 10 Python Tools For Time Series Analysis https://analyticsindiamag.com/top-10-python-tools-for-time-series-analysis/
  
  fine Tune Your Machine Learning Models To Improve Forecasting Accuracy https://www.kdnuggets.com/2019/01/fine-tune-machine-learning-models-forecasting.html
  
  add extra features https://towardsdatascience.com/the-demand-sales-forecast-technique-every-data-scientist-should-be-using-to-reduce-error-1c6f25add9cb
  
  https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
  
  https://www.machinelearningplus.com/time-series/time-series-analysis-python/  https://www.datasciencecentral.com/profiles/blogs/list-of-time-series-methods-in-one-picture
  
  https://github.com/Apress/hands-on-time-series-analylsis-python
  
  https://otexts.com/fpp2/simple-methods.html
      
  https://analyticsindiamag.com/top-time-series-deep-learning-methods/
  
  book https://otexts.com/fpp2/

 Spatial-temporal graph neural networks,Structural Deep Network Embedding,Convolutional Graph Neural Network,GraphSAGE,Graph convolutional recurrent network,Diffusion convolutional recurrent neural network,Graph LSTM,Graph Autoencoders,Variational Graph Auto-Encoders,Graph Attention Networks 

https://analyticsindiamag.com/top-8-approaches-for-tuning-hyperparameters-of-machine-learning-models/  https://analyticsindiamag.com/top-10-open-source-hyperparameter-optimisation-libraries-for-ml-models/

https://github.com/balavenkatesh3322/hyperparameter_tuning

A.manual search
 
a.GridSearchCV  (check every given parameter so take long time),TuneGridSearchCV

HalvingGridSearch https://towardsdatascience.com/11-times-faster-hyperparameter-tuning-with-halvinggridsearch-232ed0160155 https://towardsdatascience.com/faster-hyperparameter-tuning-with-scikit-learn-71aa76d06f12

tune-sklearn https://github.com/ray-project/tune-sklearn (TuneGridSearchCV)

b.RandomizedSearchCV (search randomly narrow down our time) with Scikit-learn, Scikit-Optimize,Hyperopt,TuneSearchCV

HalvingRandomSearchCV

c.Optuna,Hyperopt,Scikit-optimize,Keras Tuner,Ray-tune,Bayesian Optimization,Bayesian Optimization with Gaussian Processes (BO-GP),Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE),Particle swarm optimization (PSO),Genetic algorithm (GA)Hyperopt,bayes search,Hyperband and BOHB,HyperOpt-Sklearn,Bayes Search,Scikit Optimize,TPE,Multivariate TPE,HyperBand,Bayesian Optimization,exhaustive search, heuristic search,multi-fidelity optimization,NNI,DEAP,OptFormer,hgboost,Hyperopt,Sklearn-genetic,GPyOpt,pyGPGO,Mango,mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt,SMAC, Simulated annealing (SA),Genetic algorithms (GAs),Particle swarm optimization (PSO),Population-Based Training (PBT),Multi-Fidelity Optimization,DEAP,SMAC,Ray Tune,Google’s Vizer, Microsoft’s NNI,Keras tuner,BayesianOptimization,GPyOpt,SigOpt

Bayesian Optimization: https://github.com/fmfn/BayesianOptimization

Scikit Optimize: https://github.com/scikit-optimize/scikit-optimize

Pyro: https://github.com/pyro-ppl/pyro

BoTorch: https://github.com/pytorch/botorch

RBFOpt library for black-box optimization https://github.com/coin-or/rbfopt

Bayesian search with Gaussian processes,bayesian search with Random Forests,Bayesian search with GBMs

Bayesian Optimization Using BoTorch https://analyticsindiamag.com/guide-to-bayesian-optimization-using-botorch/

hyperparameter optimization https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms

Hyperopt hyperas https://www.kdnuggets.com/2018/12/keras-hyperparameter-tuning-google-colab-hyperas.html

hyperopt http://hyperopt.github.io/hyperopt/

hypertune-using-scikit-optimize  BayesSearchCV

HpBandSter https://github.com/automl/HpBandSter  hpsklearn https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee

hypopt  https://github.com/cgnorthcutt/hypopt https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee

HiPlot https://analyticsindiamag.com/this-new-tool-helps-developers-in-effective-hyperparameter-tuning/

botorch Bayesian optimization https://github.com/pytorch/botorch

OCTIS https://github.com/mind-lab/octis

hyperband  https://neptune.ai/blog/hyperband-and-bohb-understanding-state-of-the-art-hyperparameter-optimization-algorithms

Spearmint  https://github.com/JasperSnoek/spearmint/

tuun Hyperparameter tuning via uncertainty modeling https://github.com/petuum/tuun

tune-sklearn https://github.com/ray-project/tune-sklearn/

NeuPy http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html#id24

Vizier

ConfigSpace https://automl.github.io/ConfigSpace/master/ https://towardsdatascience.com/tuning-xgboost-with-xgboost-writing-your-own-hyper-parameters-optimization-engine-a593498b5fba

NatureInspiredSearchCV https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms

d.Sequential Model Based Optimization(Tuning a scikit-learn estimator with skopt)

e.Optuna  https://analyticsindiamag.com/hands-on-python-guide-to-optuna-a-new-hyperparameter-optimization-tool/

f.Genetic Algorithms,Gradient-based optimization

darwin-mendel Genetic Algorithm for Hyper-Parameter Tuning https://manishagrawal-datascience.medium.com/genetic-algorithm-for-hyper-parameter-tuning-1ca29b201c08

g.Keras tuner (Random Search Keras Tuner,HyperBand Keras Tuner,Bayesian Optimization Keras Tuner,Hyperas  ) https://sukanyabag.medium.com/automated-hyperparameter-tuning-with-keras-tuner-and-tensorflow-2-0-31ec83f08a62

Keras Hyperparameter Tuning with aisaratuners Library https://aisaradeepwadi.medium.com/advance-keras-hyperparameter-tuning-with-aisaratuners-library-78c488ab4d6a

hyperas  Automating Hyperparameter Tuning of Keras Model https://github.com/maxpumperla/hyperas

storm-tuner https://github.com/ben-arnao/StoRM https://medium.com/geekculture/finding-best-hyper-parameters-for-deep-learning-model-4df7a17546c2

Hyperas  https://towardsdatascience.com/automating-hyperparameter-tuning-of-keras-model-4fe69b8dedee

hyperopt-sklearn  https://github.com/hyperopt/hyperopt-sklearn

Deep AutoViML https://github.com/AutoViML/deep_autoviml

h.Scikit-Optimize,Optuna,Hyperopt,Multi-fidelity Optimization,Gradient-based optimization,Evolutionary optimization,Population-based,Bayes Search 

Scikit-Optimize library comes with BayesSearchCV implementation

mle-hyperopt Lightweight Hyperparameter Optimization Tool https://github.com/mle-infrastructure/mle-hyperopt

h.Hyperparameter Optimization  https://github.com/awslabs/syne-tune

i.ray[tune] and aisaratuners https://towardsdatascience.com/choosing-a-hyperparameter-tuning-library-ray-tune-or-aisaratuners-b707b175c1d7

raytune https://docs.ray.io/en/master/tune/index.html https://docs.ray.io/en/latest/tune/index.html

k.model_search https://github.com/google/model_search https://analyticsindiamag.com/hands-on-guide-to-model-search-a-tensorflow-based-framework-for-automl/

Optimize machine learning models https://www.tensorflow.org/model_optimization

Milano   https://github.com/NVIDIA/Milano

Tree-structured Parzen Estimators - TPE  ,  TPE with Hyperopt

Hyperparameter Tuning with the HParams Dashboard

baytune https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html

Dragonfly https://analyticsindiamag.com/guide-to-scalable-and-robust-bayesian-optimization-with-dragonfly/

Pywedge https://www.analyticsvidhya.com/blog/2021/02/interactive-widget-based-hyperparameter-tuning-and-tracking-in-pywedge/ 

CapsNet Hyperparameter Tuning with Keras https://towardsdatascience.com/scikeras-tutorial-a-multi-input-multi-output-wrapper-for-capsnet-hyperparameter-tuning-with-keras-3127690f7f28

GPyTorch: A Python Library For Gaussian Process Models https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/

Auto-PyTorch  https://github.com/automl/Auto-PyTorch

l.SMAC https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/  https://towardsdatascience.com/automl-for-fast-hyperparameters-tuning-with-smac-4d70b1399ce6

m.faster Hyper Parameter Tuning(sklearn-nature-inspired-algorithms) https://pypi.org/project/sklearn-nature-inspired-algorithms/

n.talos Neural network and hyperparameter optimization using Talos https://www.analyticsvidhya.com/blog/2021/05/neural-network-and-hyperparameter-optimization-using-talos/

https://towardsdatascience.com/10-hyperparameter-optimization-frameworks-8bc87bc8b7e3 

https://mlwhiz.com/blog/2020/02/22/hyperspark/?utm_campaign=100x-faster-hyperparameter-search-framework-with-pyspark&utm_medium=social_link&utm_source=missinglettr

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective https://github.com/microsoft/DeepSpeed

o.shap-hypetune https://github.com/cerlymarco/shap-hypetune https://towardsdatascience.com/shap-for-feature-selection-and-hyperparameter-tuning-a330ec0ea104

mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt

p.Hyperactive https://github.com/SimonBlanke/Hyperactive
   
Hyperopt, Optuna, and Ray,SCIKIT-OPTIMIZE,SMAC,Multi-fidelity Optimization,Successive Halving,Hyperband BOHB,SMBOSearch

OMLT  optimization  https://github.com/cog-imperial/OMLT

HyperOpt  http://hyperopt.github.io/hyperopt/   Optuna  https://optuna.org/   Scikit-optimize https://scikit-optimize.github.io/stable/  SigOpt  https://sigopt.com/

DeepHyper  Hyperparameter Search for Deep Neural Networks https://github.com/deephyper/deephyper

lipo  hyperparameter tuning https://github.com/jdb78/lipo

Weights and Biases to Perform Hyperparameter Optimization https://hackernoon.com/using-weights-and-biases-to-perform-hyperparameter-optimization

 1.Loocv
 
 2.Kfoldcv,Repeated K-Folds Method,Shuffle & Split cross-validation
 
 3.Stratfied cross validation,Stratified K-fold CV,Group K-fold,StratifiedGroupKFold,StratifiedShuffleSplit,Nested K-folds,Random split KFold,Walk forward,Group Time Series,Purged Group KFold,Combinatorial Purged Group KFold
 
 4.Repeated K-folds,RepeatedStratifiedKFold,Repeated random subsampling CV
 
 5.Holdout cross-validation
 
 6.Repeated cross-validation,Repeated K-folds,Blocked Cross-Validation Method, Nested Cross-Validation Method,Group Cross-Validation,GroupShuffleSplit,Blocked Cross-Validation 
 
 7.LeaveOneOut,Leave P out ,Leave-one-out cross-validation,Leave-One-Group-Out Method,Leave-P-Group-Out Method

 8.Time Series cross-validation,Time Series Split cross-validation ,Rolling Cross-Validation,Rolling Time Series Cross Validation,Rolling Window Cross-Validation,Monte Carlo Cross-Validation,Holdout Time Series Cross-Validation,Time Series Cross-Validation with a Gap,Sliding Time Series Cross-Validation,GapKFold,GapLeavePOut,GapRollForward

 9.ShuffleSplit Cross Validation,Group Shuffle Split,Simple Time Split Validation,Sliding Window Validation,Expanding Window Validation 

 10.Group KFold Cross Validation
 
 11.Monte-Carlo Cross Validation,Blocked cross-validation,Blocked K-Fold Cross-Validation,Modified K-Fold Cross-Validation

 Always check bias variance tradeoff to know how model is performing
 
 Locust Performance Testing ML Serving APIs With Locust https://www.analyticsvidhya.com/blog/2021/06/performance-testing-ml-serving-apis-with-locust/
 
 Model can be overfitting(low bias,high variance),underfitting(high bias,high variance),good fit(low bias,low variance)
 
 https://scikit-learn.org/stable/modules/model_evaluation.html   https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model
 
 https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
 
 KS test to evaluate the separation between class distribution
 
 Evaluating the potential return of a model with Lift, Gain, and Decile Analysis
 
1.Regression task - mean-squared-error, Root-Mean-Squared-Error,mean-absolute error, R², Adjusted R²,Cross-entropy loss,Mean percentage error 

2.Classification task-Accuracy,confusion matrix,Precision,Recall,F1 Score,Binary Crossentropy,Categorical Crossentropy,AUC-ROC curve,AUPRC,log loss,Average precision,Mean average precision

3.Reinforcement learning - generally  use rewards

4.Incase of machine translation use bleu score

5.Clustering then use External: Adjusted Rand index, Jaccard Score, Purity Score,Rand Index,Mutual Information,V-measure,Fowlkes-Mallows Scores,DBCV 

Internal:silhouette_score, Davies-Bouldin Index, Dunn Index

autoelbow,elbow,Davies-Bouldin Index,Calinski-Harabasz Index  

https://towardsdatascience.com/performance-metrics-in-machine-learning-part-3-clustering-d69550662dc6

6.Object Detection loss-localization loss,classification loss,Focal Loss,IOU,L2 loss

7.Distance Metrics - Euclidean Distance,Manhattan Distance,Minkowski Distance,Hamming Distance  https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa

Dimensionality Reduction Metrics - Cumulative Explained Variance,Trustworthiness,Sammon’s Mapping

8.Recommender Systems  https://parthchokhra.medium.com/evaluating-recommender-systems-590a7b87afa5

Accuracy Metrics (RMSE, MAE),Top-N Hit Rate

RecList: The better way to evaluate recommender systems

Similarity metrics : Cosine similarity,Jaccard similarity,Euclidean distance   Predictive metrics: MAE,RMSE 

metric-Built-in metrics, Custom metric without external parameters,Custom metric with external parameters,Subclassing custom metric layer

Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym

https://medium.com/swlh/custom-loss-and-custom-metrics-using-keras-sequential-model-api-d5bcd3a4ff28

loss-Built-in loss, Custom loss without external parameters,Custom loss with external parameters,Subclassing loss layer

https://analyticsindiamag.com/all-pytorch-loss-function/   https://analyticsindiamag.com/ultimate-guide-to-loss-functions-in-tensorflow-keras-api-with-python-implementation/

tensorwatch  Debugging, monitoring and visualization for Python Machine Learning and Data Science https://github.com/microsoft/tensorwatch

Types of Data Drift : Concept drift,Virtual drift,Covariate shift,Prior probability shift,Annotator drift,Data poisoning

mitigate the effects of data drift: Regular retraining,Data preprocessing,Data augmentation,Monitoring,Online learning,Domain adaptation,Annotator and data quality control

Methods to Detect Drift A) Statistical Approaches,Page-Hinkley method,Kolmogorov-Smirnov Test,Population Stability Index (PSI),Kullback-Leibler (KL) divergence,Jensen-Shannon divergence, Wasserstein Distance  B) Model-Based Approach C) Adaptive Sliding Window d)Data visualization tools e)Model performance monitoring f)Drift detection libraries

𝐭𝐨𝐨𝐥𝐬 𝐭𝐨 𝐝𝐞𝐭𝐞𝐜𝐭 𝐦𝐨𝐝𝐞𝐥 𝐝𝐫𝐢𝐟𝐭𝐬 : 𝐰𝐡𝐲𝐥𝐨𝐠𝐬,𝐄𝐯𝐢𝐝𝐞𝐧𝐭𝐥𝐲,𝐀𝐥𝐢𝐛𝐢 𝐃𝐞𝐭𝐞𝐜𝐭

Steps to take when there is an occurrence of drift Check Data Quality, Investigate,Retrain the model,Rebuild the model, Pause the model and Fallback

Ways to handle Drift in Production a) Rapidly adapt to concept drift b) Be resistant to noise while distinguishing it from concept drift c) Notice and handle severe drift in model performance.

article link https://medium.com/@dummahajan/combating-data-drift-the-fight-for-model-accuracy-2c619ee1e33a

Train: one off, batch and real-time/online training  

Serve: Batch, Realtime (Database Trigger, Pub/Sub, web-service, inApp)

Continuously Monitor the Behaviour of Deployed Models  https://se-ml.github.io/best_practices/04-monitor_models_prod/ 

Model Monitoring https://www.kdnuggets.com/2021/03/machine-learning-model-monitoring-checklist.html

Automate Model Deployment https://se-ml.github.io/best_practices/04-auto_model_packaging/

Platform as a Service (PaaS),Infrastructure as a Service (IaaS),SaaS (Software as a Service)

3 main approaches of Saving and Reloading an ML Model-Pickle Approach,Joblib Approach,JSON approach  

https://www.datacamp.com/community/tutorials/pickle-python-tutorial  https://github.com/balavenkatesh3322/model_deployment

1.Azure

2.Heroku

3.Amazon Web Services   Everything AWS  https://app.polymersearch.com/discover/aws

4.Google cloud platform

5.ngrok https://www.youtube.com/watch?v=AkEnjJ5yWV0

Deploy a Machine Learning Model for Free https://www.freecodecamp.org/news/deploy-your-machine-learning-models-for-free/

mlpack is a fast, flexible machine learning library suitable for both data science prototyping and deployment https://numfocus.org/project/mlpack  https://github.com/mlpack/mlpack

MODEL DEPLOYMENT USING TF SERVING

Dockerize  https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html  https://pub.towardsai.net/how-to-dockerize-your-data-science-project-a-quick-guide-b6fa2d6a8ba1

bodywork-core MLOps tool for deploying machine learning projects to Kubernetes https://github.com/bodywork-ml/bodywork-core

Create ML model inside the docker container https://dev.to/niteshthapliyal/create-ml-model-inside-the-docker-container-3b23

LyftLearn: ML Model Training Infrastructure built on Kubernetes https://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb

Model Serving https://neptune.ai/blog/ml-model-serving-best-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-ml-model-serving-best-tools
 
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines https://www.tensorflow.org/tfx  https://theaisummer.com/tfx/?utm_content=163294295&utm_medium=social&utm_source=linkedin&hss_channel=lcp-42461735

torchblaze https://github.com/MLH-Fellowship/torchblaze https://mlh-fellowship.github.io/torchblaze/

ML Aide Manage Machine Learning Lifecycle  https://mlaide.com/home https://medium.com/ml-aide/manage-machine-learning-lifecycle-with-ml-aide-dfe7710cbe53

Models visualization using Tensorboard,netron, TensorBoard.dev

Python web Frameworks for App Development- Flask,Streamlit,fastapi,fastDeploy,Django,Web2py,Pyramid,CherryPy,Voila,Kivy and Kivymd  

streamlit,Gradio,mia,opyrator,plotly jupyterdash,h2o wave,dash,gradio,PyWebIO,r shiny,sanic,panel,flask,django,PySimpleGUI,pywebio,autocalc,Mercury,Chitra ,Bokeh,Panel,jupyter Voila with ipywidgets,Panel,dash,Fast Dash,BentoML,Cortex,Seldon,UnionML,Taipy,fastDeploy,Mlflow,Seldon core,tensorflow serving,kserve,torchserve,ray,clearml,mlrun,pymlpipe,FastDeploy,Shiny,Voila,Cog,BentoML,MLflow,PyMLpipe,truss,playtorch,Streamsync,panel,Databutton,plotly,pyscript, Sanic,skops,Mage,sematic,Cog, BentoML,Truss,bentoctl,Banana,Pyramid,Docker,Kubernetes,SageMaker,TensorFlow Serving,Kubeflow,Cortex,Seldon.io,Cortex,TensorFlow Serving,TorchServe,KFServing,Multi Model Server,Triton Inference Server,ForestFlow,Seldon Core,BudgetML,GraphPipe,Hydrosphere,MLEM,Opyrator,Apache PredictionIO,Cortex,Triton Inference Server,ForestFlow,DeepDetect,Seldon Core,Kubeflow,datapane,Pynecone.io,Anvil,h2oai nitro,rest-model-service,Databutton,CherryPy,Anvil,modelbit,Pynecone,modelbit,wagtail,flet,Chainlit,Solara


Django models https://www.deploymachinelearning.com/#create-django-models  https://www.deploymachinelearning.com/

BentoML https://github.com/bentoml/BentoML

UnionML: the easiest way to build and deploy machine learning microservices https://github.com/unionai-oss/unionml

panel high-level app and dashboarding solution for Python https://github.com/holoviz/panel

sanic https://github.com/sanic-org/sanic

Gradio - take input from user  https://gradio.app/getting_started

Fast Dash https://fastdash.app/

binder - https://mybinder.org/

Netlify  https://www.analyticsvidhya.com/blog/2021/04/easily-deploy-your-machine-learning-model-into-a-web-app-netlify/

streamlit https://www.kdnuggets.com/2019/10/write-web-apps-using-simple-python-data-scientists.html  https://www.youtube.com/watch?v=iUgNIFrVejc   https://blog.streamlit.io/introducing-theming/

Streamlit Flask App from Colab using remoteit and ngrok https://www.youtube.com/watch?v=O2enoygZwl4

Streamlit to databases https://docs.streamlit.io/en/0.83.0/tutorial/databases.html

https://github.com/jrieke/best-of-streamlit

https://neptune.ai/blog/streamlit-guide-machine-learning?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-streamlit-guide-machine-learning

streamlit-ace https://github.com/okld/streamlit-ace  https://www.youtube.com/watch?v=Iv2vt-7AYNQ 

customize the themes of your Streamlit web apps https://www.youtube.com/watch?v=3xJYP_C4KNE https://github.com/khuyentran1401/Data-science/tree/master/applications/pywebio_examples

colab_everything Python library to run streamlit, flask, fastapi, etc on google colab  https://github.com/Ankur-singh/colab_everything/

dash https://github.com/plotly/dash

panel-highcharts https://awesome-panel.org/  https://github.com/marcskovmadsen/panel-highcharts https://github.com/holoviz/panel https://github.com/holoviz/panel

opyrator Turns your machine learning code into microservices with web API, interactive GUI, and more  https://github.com/ml-tooling/opyrator

plotly https://plotly.com/  https://analyticsindiamag.com/how-to-use-plotly-in-colab/

Creating a Machine Learning App with Power BI and PyCaret

Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter vs django vs PySimpleGUIvs pywebio vs Gradio vs autocalc vs Mercury vs Chitra  https://www.datarevenue.com/en-blog/data-dashboarding-streamlit-vs-dash-vs-shiny-vs-voila,pymlpipe,Lightning Apps,Aibro

Mercury: easily convert Python notebook to web app and share with others https://github.com/mljar/mercury

autocalc https://github.com/kefirbandi/autocalc https://towardsdatascience.com/creating-a-ui-with-ipywidgets-and-autocalc-2ef8ea4cc6c2

Quickly deploy ML WebApps https://ngrok.com/

Chitra https://github.com/gradsflow/chitra

Deepnote https://deepnote.com/  https://www.youtube.com/watch?v=0ppptVxgEI8

booklet https://booklet.ai/  https://towardsdatascience.com/building-a-covid-19-project-recommendation-system-4607806923b9

https://analyticsindiamag.com/top-8-python-tools-for-app-development/

Voila This library can turn your Jupyter notebooks into standalone web apps that can be deployed to any cloud platform.  https://voila.readthedocs.io/en/stable/

H2O.ai https://www.h2o.ai/blog/data-to-production-ready-models-to-business-apps-in-just-a-few-steps/ 

PyQt and Tkinter , PySimpleGUI are GUI programming in Python  https://github.com/tirthajyoti/DS-with-PySimpleGUI

DearPyGui https://github.com/hoffstadt/DearPyGui

PySimpleGUI https://github.com/PySimpleGUI/PySimpleGUI

Gooey Turn (almost) any Python command line program into a full GUI application with one line https://github.com/chriskiehl/Gooey

snapyml Deploy AI Models For Free -http://snapyml.snapy.ai/

BentoML https://github.com/bentoml/BentoML

h20 wave-apps https://github.com/h2oai/wave-apps  https://h2oai.github.io/wave/docs/installation/  https://h2oai.github.io/wave/

h20 Wave ML (AutoML for Wave Apps) https://h2oai.github.io/wave/blog/ml-release-0.3.0/

fastapi https://towardsdatascience.com/deploying-ml-models-in-production-with-fastapi-and-celery-7063e539a5db

FastAPI + Uvicorn https://www.kdnuggets.com/2021/04/deploy-machine-learning-models-to-web.html  

FastAPI based template https://github.com/99sbr/fastapi-template  fastapi-log 0.0.3 https://pypi.org/project/fastapi-log/

testing FastAPI ML APIs with Locust https://locust.io/  https://rubikscode.net/2022/03/21/performance-testing-fastapi-ml-apis-with-locust/

chitra 𝗖𝗿𝗲𝗮𝘁𝗲 𝗔𝗣𝗜 𝗳𝗼𝗿 𝗔𝗻𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹  https://github.com/aniketmaurya/chitra

PyWebIO Write Interactive Web App in Script Way Using Python https://www.youtube.com/watch?v=vp1ZNapAy6Y  https://towardsdatascience.com/pywebio-write-interactive-web-app-in-script-way-using-python-14f50155af4e  https://github.com/tirthajyoti/PyWebIO

aibro Deploy Machine Learning Models to the Cloud Quickly and Easily https://aipaca.ai/?ref=hackernoon.com  https://medium.datadriveninvestor.com/how-to-deploy-machine-learning-models-to-the-cloud-quickly-and-easily-41cca9425c75

Katana https://github.com/shaz13/katana https://katana-demo.herokuapp.com/redoc  https://katana-demo.herokuapp.com/docs

DS-with-PySimpleGUI  https://github.com/tirthajyoti/DS-with-PySimpleGUI

pywinauto Windows GUI Automation with Python

tkinter to deploy machine learning model-https://analyticsindiamag.com/complete-tutorial-on-tkinter-to-deploy-machine-learning-model/

Tkinter-Designer Create Beautiful Tkinter GUIs by Drag and Drop https://github.com/ParthJadhav/Tkinter-Designer

Web-Based GUI (Gradio)- https://analyticsindiamag.com/guide-to-gradio-create-web-based-gui-applications-for-machine-learning/  https://www.gradio.app/

Bamboolib https://medium.com/ai-in-plain-english/bamboolib-a-data-warriors-weapon-9f734f4c2553

web application(dash)- https://dash.plotly.com/

Pyramid web framework https://trypyramid.com/documentation.html

Kivy /Kivymd creating an android app 

https://towardsdatascience.com/pycaret-2-1-is-here-whats-new-4aae6a7f636a

Create a Website with AI https://www.bookmark.com/ 

localhost to globalurl https://ngrok.com/  https://remote.it/
 
Jupyter Notebook into an interactive dashboard (voila)-https://voila.readthedocs.io/en/stable/

high-level app and dashboarding solution(Panel)-https://panel.holoviz.org/

MaaS Build ML Models As A Service  https://www.analyticsvidhya.com/blog/2021/05/maas-build-ml-models-as-a-service/

https://github.com/gradio-app/gradio

1.COURSERA

2.UDEMY

3.EDX

4.DATACAMP

5.Udacity

6.https://www.skillbasics.com/ 

1.Krish Naik-https://www.youtube.com/user/krishnaik06

2.Codebasics-https://www.youtube.com/channel/UCh9nVJoWXmFb7sLApWGcLPQ  

3.Abhishek thakur-https://www.youtube.com/user/abhisheksvnit

4.AIEngineering-https://www.youtube.com/channel/UCwBs8TLOogwyGd0GxHCp-Dw

5.Ineuron-https://www.youtube.com/channel/UCb1GdqUqArXMQ3RS86lqqOw

6.Ken jee-https://www.youtube.com/c/KenJee1/featured       

7.3Blue1Brown-https://www.youtube.com/c/3blue1brown/featured

8.The AI Guy -https://www.youtube.com/channel/UCrydcKaojc44XnuXrfhlV8Q 

9.Unfold Data Science-https://www.youtube.com/channel/UCh8IuVJvRdporrHi-I9H7Vw  etc...

https://www.cybrhome.com/topic/data-science-blogs    

AI Summary https://ai-summary.com/

https://www.datasciencecentral.com/profiles/blog/list  https://developer.nvidia.com/blog/?ncid=em-prom-48627

1.Towards data science-https://towardsdatascience.com/

2.Analyticsvidhya-https://www.analyticsvidhya.com/blog/?utm_source=feed&utm_medium=navbar       https://analyticsindiamag.com/  https://www.analyticsinsight.net/

3.Medium-https://medium.com/

4.Machinelearningmastery-https://machinelearningmastery.com/blog/

5.ML+  -https://www.machinelearningplus.com/

6.analyticsinsight https://www.analyticsinsight.net/category/latest-news/    https://www.analyticsinsight.net/

7.KDnuggets https://www.kdnuggets.com/  https://www.kdnuggets.com/news/index.html   

8.Artificial Intelligence Database https://www.wired.com/category/artificial-intelligence/?verso=true

https://machinelearningknowledge.ai/   

https://github.com/rushter/data-science-blogs

https://www.datamuni.com/

https://blog.ml.cmu.edu/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow

https://www.amazon.science/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog&f0=0000016e-2ff1-d205-a5ef-aff9651e0000&s=0

https://distill.pub/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow

https://ai.googleblog.com/search/label/Machine%20Learning?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow

https://neptune.ai/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog

https://bair.berkeley.edu/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow

https://deepmind.com/research?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow&filters=%7B%22category%22:%5B%22Research%22%5D%7D

https://ai.facebook.com/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow

https://becominghuman.ai/top-25-ai-and-machine-learning-blogs-for-data-scientists-9f121bcfd9a2

https://medium.com/towards-artificial-intelligence/best-machine-learning-blogs-to-follow-ml-research-ai-3994e01967f9

Processing documents as Text: extract text with PyPDF2, extract tables with Camelot or TabulaPy, extract figures with PyMuPDF.

Converting documents into Image (OCR): conversion with pdf2image, extract data with PyTesseract plus many other supporting libraries, or just LayoutParser.

OCR toolbox from Davar-Lab https://github.com/hikopensource/davar-lab-ocr

To pdf: python-pdfkit,wkhtmltopdf,FPDF

   knockknock https://towardsdatascience.com/how-to-get-notified-when-your-model-is-done-training-with-knockknock-483a0475f82c
   
   labmi Organize machine learning experiments and monitor training progress from mobile  https://labml.ai/
   
   WeightWatcher https://github.com/CalculatedContent/WeightWatcher
   
   labml Monitor deep learning model training and hardware usage from your mobile phone https://labml.ai/      https://github.com/labmlai/labml
   
   ml notify https://github.com/aporia-ai/mlnotify

1.Linkedin

2.Twitter

1.Google cloab (FREE)  Jupyter Lab for Python, R, Swift from Google Colab with ColabCode  https://www.youtube.com/watch?v=Q35WIqZoUF4

https://www.analyticsvidhya.com/blog/2021/01/avid-user-of-google-colab-here-are-some-alternatives-of-google-colab-that-you-should-know-about/?utm_source=linkedin&utm_medium=social&utm_campaign=old-blog&utm_content=B&custom=FBI156

https://towardsdatascience.com/use-colab-more-efficiently-with-these-hacks-fc89ef1162d8 https://www.analyticsvidhya.com/blog/2021/05/10-colab-tips-and-hacks-for-efficient-use-of-it/

ColabCode This is an amazing extension to the already available resource, Google Colab  https://github.com/abhi1thakur/colabcode 

GitHub notebooks with Google Colab https://www.youtube.com/watch?v=LmIylxNmA-A&feature=youtu.be  

colab_everything Python library to run streamlit, flask, fastapi, etc on google colab  https://github.com/Ankur-singh/colab_everything/

2.Kaggle kernel(read terms and conditions before use) (FREE)

3.Paperspace Gradient(read terms and conditions before use)

4.knime - https://www.knime.com/(read terms and conditions before use)

5.RapidMiner (read terms and conditions before use)

https://github.com/zszazi/Deep-learning-in-cloud

6.saturncloud  https://saturncloud.io/

Intel Jupyter Lab,Amazon Sagemaker,Binder,DeepNote,Hex,DataBricks Notebook,Jetbrains Datalore,DataCamp Workspace,Notablejournal,Notable,Observable,CoCalc,Replit,Binder,IBM DataPlatform Notebooks,CodeSandbox,StackBlitz

1.E-commerce- Identifying consumers,Recommending Products,Analyzing Reviews

2.Manufacturing- Predicting potential problems,Monitoring systems,Automating manufacturing units, Maintenance Scheduling,Anomaly Detection

3.Banking- Fraud detection,Credit risk modeling,Customer lifetime value

4.Healthcare- Medical image analysis, Drug discovery,Bioinformatics,Virtual Assistants,image segmentation

5.Transport- Self-driving cars,Enhanced driving experience,Car monitoring system,Enhancing the safety of passengers

6.Finance- Customer segmentation,Strategic decision making,Algorithmic trading,Risk analytics

7.Marketing (Added from comments Credits: Jawad Ali)- LTV predictions,Predictive analytics for customer behavior,Ad targeting

and many more fields - https://www.topbots.com/enterprise-ai-companies-2020/ , https://venturebeat.com/2020/10/21/the-2020-data-and-ai-landscape/

                                                    HAPPY LEARNING