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A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months

ML-Roadmap-for-2022

A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months

Levels of Learning

  1. Testing the waters

  2. Gaining Conceptual depth

  3. Learning Practical Concepts

  4. Diving into different domains

  5. Pushing it with Projects

1. Testing the waters (Est. time 6-8 Weeks)

The goal of this level is to get you familiar with the ML universe. You will learn a bit of everything.

  1. Learn Python (Est. time - 2 weeks)

     1. Basics of Python - https://www.youtube.com/playlist?list=PLKnIA16_Rmvb1RYR-iTA_hzckhdONtSW4
 2. OOP in Python
    - Lecture 1 - https://www.youtube.com/watch?v=1s869EfxoDo
    - Lecture 2 - https://www.youtube.com/watch?v=8To-A6VPL90
 3. Advance Topics
    - File Handling - https://www.youtube.com/watch?v=ixEeeNjjOJ0
    - Exception Handling - https://www.youtube.com/watch?v=NIWwJbo-9_8
    - Regular Expressions - https://www.youtube.com/watch?v=K8L6KVGG-7o
    - Functional Programming - https://www.youtube.com/watch?v=SvK_GErE2nM
    - Basics of Flask - https://www.youtube.com/watch?v=swHI1H7DVsQ
 4. Practice Problems - https://docs.google.com/document/d/1E_xCNijOWZ4Bm7r7DVj-1OA-oUopEFmv4tRm0YNuFWQ/edit?usp=sharing

  2. Learn Numpy (Est. time 3 Days)

     1. Numpy Playlist - https://www.youtube.com/watch?v=CpPLLp3snK4&list=PLKnIA16_Rmvb-ToL3RQ_bwxG4_ND-0-DT
 2. Numpy Practice Problems - https://github.com/rougier/numpy-100

  3. Learn Pandas (Est. time 4 Days)

     1. Pandas Playlist - https://www.youtube.com/watch?v=kq9Vmg5d7Sk&list=PLKnIA16_RmvbR85fgbfVRKOiMokUKVupy
 2. Pandas Problems - https://github.com/ajcr/100-pandas-puzzles

  4. Learn Data Visualization (Est. time 1 Week)

     1. Matplotlib - https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_
 2. Seaborn - https://www.youtube.com/playlist?list=PLKnIA16_RmvbB1bFGjvS6a8T0mnqawejo

  5. Descriptive Statistics (Est. time 4 Days)

     1. Statistics Playlist - https://www.youtube.com/watch?v=tPhzDKjQBpo&list=PLKnIA16_RmvbVrE0eZO2bCaFln6jaNq-1

  6. Learn Data Analysis Process (Est. time 1 week)

     1. Playlist - https://www.youtube.com/watch?v=ZhacwtUR0SU&list=PLKnIA16_RmvZAqJzKstVHywcRNMn6pcGD

  7. Learn Exploratory Data Analysis (EDA) (Est. time 1 Week)

    1. Understanding your data - https://www.youtube.com/watch?v=mJlRTUuVr04
2. Univariate Analysis - https://www.youtube.com/watch?v=4HyTlbHUKSw
3. Bivariate and Multivariate Analysis - https://www.youtube.com/watch?v=6D3VtEfCw7w
4. Pandas Profiling - https://www.youtube.com/watch?v=E69Lg2ZgOxg
5. EDA on House Prices Dataset - https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python
6. EDA on Titanic Dataset - https://www.kaggle.com/startupsci/titanic-data-science-solutions
7. EDA on Haberman's Survival Dataset - https://www.kaggle.com/gokulkarthik/haberman-s-survival-exploratory-data-analysis
8. EDA on Heart Disease Dataset - https://www.kaggle.com/kralmachine/analyzing-the-heart-disease
9. EDA on IPL Dataset - https://www.kaggle.com/ash316/let-s-play-cricket
10. EDA on Wine Review Dataset - https://www.kaggle.com/kabure/wine-review-s-eda-recommend-systems
11. EDA on PIMA Diabetes Dataset - https://www.kaggle.com/shrutimechlearn/step-by-step-diabetes-classification-knn-detailed
12. EDA on Breast Cancer Dataset - https://www.kaggle.com/kanncaa1/statistical-learning-tutorial-for-beginners
13. EDA on Olympics Dataset - https://www.youtube.com/watch?v=5nQXhusiu7s
14. EDA on Covid Data - https://www.youtube.com/watch?v=ll0aZVNnOP8
15. WhatsApp Chat Analysis Project - https://www.youtube.com/watch?v=Q0QwvZKG_6Q

  8. Learn Machine Learning Basics (Est. time 1 Week)

     1. What is Machine Learning? https://www.youtube.com/watch?v=ZftI2fEz0Fw
 2. AI vs ML vs DL https://www.youtube.com/watch?v=1v3_AQ26jZ0
 3. Types of Machine Learning - https://www.youtube.com/watch?v=81ymPYEtFOw
 4. Batch Machine Learning - https://www.youtube.com/watch?v=nPrhFxEuTYU
 5. Online Machine Learning - https://www.youtube.com/watch?v=3oOipgCbLIk
 6. Instance based vs Model based learning - https://www.youtube.com/watch?v=ntAOq1ioTKo
 7. Challenges in Machine Learning - https://www.youtube.com/watch?v=WGUNAJki2S4
 8. Applications of Machine Learning - https://www.youtube.com/watch?v=UZio8TcTMrI
 9. Machine Learning Development Lifecycle - https://www.youtube.com/watch?v=iDbhQGz_rEo
 10. Data Engineer V Data Analyst V Data Scientist V ML Engineer - https://www.youtube.com/watch?v=93rKZs0MkgU
 11. How to frame a Machine Learning problem? - https://www.youtube.com/watch?v=A9SezQlvakw
 12. Installing and using software for data science - https://www.youtube.com/watch?v=82P5N2m41jE
 13. How to work with CSV files? - https://www.youtube.com/watch?v=a_XrmKlaGTs
 14. Working with JSON and SQL data - https://www.youtube.com/watch?v=fFwRC-fapIU
 15. Building an End to End Machine Learning Project - https://www.youtube.com/watch?v=dr7z7a_8lQw

2. Gaining Conceptual depth (Est. time 6-8 Weeks)

The goal of this level is to learn the core machine learning concepts and algorithms

  1. Learn about tensors (Est. time - 1 Day)

     1. What are Tensors? - https://www.youtube.com/watch?v=vVhD2EyS41Y

  2. Advance Statistics

     1. Covariance
 2. Pearson Correlation Coefficient
 3. QQ Plot
 4. Confidence Interval
 5. Hypothesis Testing
 6. Chisquare Test, Anova Test
 7. Playlist link - https://www.youtube.com/watch?v=qtaqvPAeEJY&list=PLKnIA16_Rmvbe9wDJGXc28KKr6lp5Jn2g

  3. Probability Basics

     1. Conditional Probability
 2. Independent Events
 3. Bayes Theorem
 4. Uniform Distribution
 5. Binomial Distribution
 6. Bernaulli Distribution
 7. Poission Distribution
 8. Playlist Link - https://www.youtube.com/watch?v=Ty7knppVo9E&list=PLKnIA16_RmvYNbPMB6ofVLRCcTPUAftdY

  4. Linear Algebra Basics

     1. Representing Tabular Data
 2. Vectors
 3. Matrices
 4. Matrix Multiplication
 5. Dot Product
 6. Equation of line in N-dim
 7. Eigen Vector and Eigen Values
 8. Playlist Link - https://www.youtube.com/watch?v=e9h-ZZ_ahRg&list=PLKnIA16_RmvYu0fS_RuIB2eTbJcTFdrAA

  5. Basics of Calculus

     1. Big Picture of Derivatives
 2. Maxima and Minima
 3. Playlist link - (first 4 videos only) https://www.youtube.com/playlist?list=PLBE9407EA64E2C318

  6. Machine Learning Algorithms

     1. Linear Regression - https://www.youtube.com/watch?v=UZPfbG0jNec&list=PLKnIA16_Rmva-wY_HBh1gTH32ocu2SoTr
 2. Gradient Descent - https://www.youtube.com/watch?v=ORyfPJypKuU&list=PLKnIA16_RmvZvBbJex7T84XYRmor3IPK1
 3. Logistic Regression - https://www.youtube.com/watch?v=XNXzVfItWGY&list=PLKnIA16_Rmvb-ZTsM1QS-tlwmlkeGSnru
 4. Support Vector Machines - https://www.youtube.com/watch?v=ugTxMLjLS8M&list=PLKnIA16_RmvbOIFee-ra7U6jR2oIbCZBL
 5. Naive Bayes - https://www.youtube.com/watch?v=Ty7knppVo9E&list=PLKnIA16_RmvZ67wQaHoBuzXaDAfPz-a6l
 6. K Nearest Neighbors - https://www.youtube.com/watch?v=BYaoDZM1IcU&list=PLKnIA16_RmvZiE-lEdN5RDi18-u-T43zd
 7. Decision Trees - https://www.youtube.com/watch?v=gwgmSSTdiXs&list=PLKnIA16_RmvYGY_n9PP8zN-0LG9MoZRjU
 8. Random Forest - https://www.youtube.com/watch?v=bHK1fE_BUms&list=PLKnIA16_RmvZyqP3WGUo7iVziIIea_1bp
 9. Bagging - https://www.youtube.com/watch?v=LUiBOAy7x6Y&list=PLKnIA16_RmvZ7iKIcJrLjUoFDEeSejRpn
 10. Adaboost - https://www.youtube.com/watch?v=sFKnP0iP0K0&list=PLKnIA16_RmvZxriy68dPZhorB8LXP1PY6
 11. Gradient Boosting - https://www.youtube.com/watch?v=fbKz7N92mhQ&list=PLKnIA16_RmvaMPgWfHnN4MXl3qQ1597Jw
 12. Xgboost - https://www.youtube.com/watch?v=BTLB-ppqBZc&list=PLKnIA16_RmvbXJbBW4zCy4Xbr81GRyaC4
 13. Principle Component Analysis (PCA) - https://www.youtube.com/watch?v=ToGuhynu-No&list=PLKnIA16_RmvYHW62E_lGQa0EFsph2NquD
 14. KMeans Clustering - https://www.youtube.com/watch?v=5shTLzwAdEc&list=PLKnIA16_RmvbA_hYXlRgdCg9bn8ZQK2z9
 15. Heirarchical Clustering - https://www.youtube.com/watch?v=Ka5i9TVUT-E
 16. DBSCAN - https://www.youtube.com/watch?v=RDZUdRSDOok
 17. T-sne - https://www.youtube.com/watch?v=NEaUSP4YerM and https://distill.pub/2016/misread-tsne/

  7. Machine Learning Metrics - https://www.youtube.com/watch?v=Ti7c-Hz7GSM&list=PLKnIA16_RmvZJGOqRjqhOhTEmQW3vDdbQ

  8. Bias Variance Tradeoff - https://www.youtube.com/watch?v=74DU02Fyrhk

  9. Regularization - https://www.youtube.com/watch?v=aEow1QoTLo0&list=PLKnIA16_RmvZuSEZ24Wlm13QpsfLlJBM4

  10. Cross-Validation - https://www.youtube.com/watch?v=S5NkE-xgx98

3. Learn Practical Concepts (Est. time 6-8 Weeks)

The goal of this level is to get you introduced to the practical side of machine learning. What you learn at this level would really help you out there in the wild.

  1. Data Acquisition (Est. time - 2 Days)

     1. Web Scraping - https://www.youtube.com/watch?v=8NOdgjC1988
         * Project - Create a Pandas dataframe of Indian cuisines from some website using web scraping.
 2. Fetch data from API - https://www.youtube.com/watch?v=roTZJaxjnJc
         * Project - Create a Pandas dataframe of movies from TMDB API.

  2. Working with missing values (Est. time - 3 Days)

     1. Complete Case Analysis - https://www.youtube.com/watch?v=aUnNWZorGmk
 2. Handling missing numerical data - https://www.youtube.com/watch?v=mCL2xLBDw8M
 3. Handling missing categorical data - https://www.youtube.com/watch?v=l_Wip8bEDFQ
 4. Missing indicator - https://www.youtube.com/watch?v=Ratcir3p03w
 5. KNN Imputer - https://www.youtube.com/watch?v=-fK-xEev2I8
 6. MICE - https://www.youtube.com/watch?v=a38ehxv3kyk
 7. Kaggle Notebooks and Practice Datasets - https://docs.google.com/document/d/1_9Y6kxNc6QTym2Y2JGEBbnCUbE1qZWLVzVXlT2eX_FQ/edit?usp=sharing

  3. Feature Scaling/Normalization (Est. time - 2 Days)

     1. Standarization - https://www.youtube.com/watch?v=1Yw9sC0PNwY
 2. Normalization - https://www.youtube.com/watch?v=eBrGyuA2MIg

  4. Feature Encoding Techniques (Est. time - 2 Days)

     1. Ordinal Enconding and Label Encoding - https://www.youtube.com/watch?v=w2GglmYHfmM
 2. One Hot Encoding - https://www.youtube.com/watch?v=U5oCv3JKWKA
 3. Encoding high cardinality categorical features - https://www.kaggle.com/general/16927
 4. Feature hashing - https://datasciencestunt.com/dealing-with-categorical-features-with-high-cardinality-feature-hashing/

  5. Feature Transformation(Est. time - 2 Days)

     1. Log Transform - https://www.youtube.com/watch?v=cTjj3LE8E90
 2. Box Cox Transform - https://www.youtube.com/watch?v=lV_Z4HbNAx0
 3. Yeo Johnson Transform - https://www.youtube.com/watch?v=lV_Z4HbNAx0
 4. Discretization - https://www.youtube.com/watch?v=kKWsJGKcMvo

  6. Working with Pipelines(Est. time - 2 Days)

    1. Column Transformer - https://www.youtube.com/watch?v=5TVj6iEBR4I
2. Sklearn Pipelines - https://www.youtube.com/watch?v=xOccYkgRV4Q

  7. Handing Time and Date data(Est. time - 1 Day)

    1. Working with time and date data - https://www.youtube.com/watch?v=J73mvgG9fFs

  8. Working with Outliers (Est. time - 3 Days)

    1. What are Outliers? - https://www.youtube.com/watch?v=Lln1PKgGr_M
2. Outlier detection and removal using Z-score method - https://www.youtube.com/watch?v=OnPE-Z8jtqM
3. Outlier detection and removal using IQR method - https://www.youtube.com/watch?v=Ccv1-W5ilak
4. Percentile method - https://www.youtube.com/watch?v=bcXA4CqRXvM

  9. Feature Construction (Est. time - 1 Day)

    1. Feature Construction - https://www.youtube.com/watch?v=ma-h30PoFms

  10. Feature Selection (Est. time - 3 Days)

     1. Feature selection using SelectKBest and Recursive Feature Elimination - https://www.youtube.com/watch?v=xlHk4okO8Ls&t=1s
 2. Chi-squared Feature Selection - https://www.youtube.com/watch?v=fMIwIKLGke0
 3. Backward Feature Elimination - https://www.youtube.com/watch?v=zW1SvA0Z-l4&t=2s
 4. Dropping features using Pearson correlation coefficient - https://www.youtube.com/watch?v=FndwYNcVe0U
 5. Feature Importance using Random Forest - https://www.youtube.com/watch?v=R47JAob1xBY
 6. Feature Selection Advise - https://www.youtube.com/watch?v=YaKMeAlHgqQ

  11. Cross Validation (Est. time - 2 Days)

     1. What is cross-validation? - https://www.youtube.com/watch?v=fSytzGwwBVw
 2. Holdout Method - https://www.youtube.com/watch?v=4NnI3SBuww4
 3. K-Fold Cross Validation - https://www.youtube.com/watch?v=gJo0uNL-5Qw
 4. Leave 1 Out Cross Validation - https://www.youtube.com/watch?v=yxqcHWQKkdA
 5. Time series cross validation - https://www.youtube.com/watch?v=g9iO2AwTXyI

  12. Modelling - Stacking and Blending (Est. time - 1 Week)

     1. Stacking - https://www.youtube.com/watch?v=O-aDHBGMqXA
 2. Blending - https://www.youtube.com/watch?v=TuIgtitqJho
 3. LightGBM - https://www.youtube.com/watch?v=n_ZMQj09S6w
 4. CatBoost - https://www.youtube.com/watch?v=8o0e-r0B5xQ

  13. Model Tuning (Est. time - 4 Days)

     1. GridSearchCV - https://www.youtube.com/watch?v=4Im0CT43QxY
 2. RandomSearchCV - https://www.youtube.com/watch?v=Q5dH5mOQ_ik
 3. Hyperparameter Tuning - https://www.youtube.com/watch?v=355u2bDqB7c

  14. Working with imbalanced data (Est. time - 3 Days)

     1. How to handle imbalanced data - https://www.youtube.com/watch?v=JnlM4yLFNuo
 2. Kaggle Notebook - https://www.kaggle.com/kabure/credit-card-fraud-prediction-rf-smote
 3. SMOTE on Quora Dataset - https://www.kaggle.com/theoviel/dealing-with-class-imbalance-with-smote

  15. Handling Multicollinearity(Est. time - 2 Days)

     1. What is multicollinearity? - https://www.youtube.com/watch?v=ekuD8JUdL6M
 2. Practical Example - https://www.youtube.com/watch?v=ATH4urDitI8
 3. VIF in Multicollinearity - https://www.youtube.com/watch?v=GMAp_tP1ZQ0

  16. Data Leakage - (Est. time - 2 Days)

     1. What is Data Leakage? - https://machinelearningmastery.com/data-leakage-machine-learning/
 2. Practical - Data Leakage on Quora Question Pair Dataset - https://www.kaggle.com/sudalairajkumar/simple-leaky-exploration-notebook-quora
 3. Practical - Data Leakage on Credit Card data - https://www.kaggle.com/dansbecker/data-leakage

  17. Serving your model(Est. time - 1 Week)

     1. Pickling your model - https://www.youtube.com/watch?v=yY1FXX_GSco
 2. Flask Tutorial - https://www.youtube.com/watch?v=swHI1H7DVsQ
 3. Streamlit Tutorial - https://www.youtube.com/watch?v=Klqn--Mu2pE
 4. Deploy model on Heroku - https://www.youtube.com/watch?v=YncZ0WwxyzU
 5. Deploy model on AWS - https://www.youtube.com/watch?v=_rwNTY5Mn40
 6. Deploy model to GCP - https://www.youtube.com/watch?v=fw6NMQrYc6w
 7. Deploy model to Azure - https://www.youtube.com/watch?v=qnbJcbjh-3s
 8. ML model to Android App - https://www.youtube.com/watch?v=ax3WyB-_LJY

  18. Working with Large Datasets

     1. What is Out of core ML? - https://www.youtube.com/watch?v=9e4nUuq2Hmg
 2. Practical implementation of Out of core ML - https://www.youtube.com/watch?v=sRCuvcdvuzk
 3. NYC Cab Dataset Project - https://vaex.io/blog/ml-impossible-train-a-1-billion-sample-model-in-20-minutes-with-vaex-and-scikit-learn-on-your

4. Diving into different domains (Est. time 6-8 Weeks)

This is the level where you would dive into different domains of Machine Learning. Mastering these will make you a true Data Scientist.

  1. SQL (Est. time - 2 Days)

     1. Complete SQL Roadmap - https://www.youtube.com/watch?v=FGBme8dWR_M
 2. SQL learning resources - https://docs.google.com/document/d/1wCALgWubTOvuvlXJ3Eweh7AgJj4sPq2pW92y3viPZbs/edit?usp=sharing
 3. The only video you need to see - https://www.youtube.com/watch?v=nopIGY1zJE0

  2. Recommendation Systems

     1. Movie Recommendation System - https://www.youtube.com/watch?v=1xtrIEwY_zY
 2. Book Recommender System - https://www.youtube.com/watch?v=sf93xpq8vaA
 3. Fashion Recommender System - https://www.youtube.com/watch?v=xanJe6e8Xuw

  3. Association Rule Learning

     1. Association Rule Mining(Apriori Algorithm) - https://www.youtube.com/watch?v=guVvtZ7ZClw
 2. Eclat Algorithm - https://www.youtube.com/watch?v=oBiq8cMkTCU
 3. Market Basket Analysis - https://www.youtube.com/watch?v=Y7Xkqqfz1UU

  4. Anamoly Detection

     1. Anamoly Detection Lecture from Microsoft Research - https://www.youtube.com/watch?v=12Xq9OLdQwQ
 2. Novelty Detection Lecture - https://www.youtube.com/watch?v=vIDcjbpwY3k

  5. NLP

     1. Complete NLP Roadmap - https://www.youtube.com/watch?v=PKv_okm1H-k
 2. Complete NLP Playlist - https://www.youtube.com/watch?v=zlUpTlaxAKI&list=PLKnIA16_RmvZo7fp5kkIth6nRTeQQsjfX
 3. NLP Project Ideas - https://www.youtube.com/watch?v=oWJe2T29kAo
 4. Email Spam Classifier Project - https://www.youtube.com/watch?v=YncZ0WwxyzU
 5. Building a Chatbot - https://www.youtube.com/watch?v=Nb21OhaW8GY

  6. Time Series(Coming Soon)

  7. Computer Vision(Coming Soon)

  8. Fundamentals of Neural Network - https://www.youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn

5. Pushing it with Projects (Est. time 6-8 Weeks)

The objective of this level is to sharpen your knowledge that you have accumulated in the previous 4 levels

  1. 8 types of Projects for your portfolio - https://www.youtube.com/watch?v=SQHfry4xmdM
  2. How to select a project - https://www.youtube.com/watch?v=kH--k1VKFt4
  3. Car Price Predictor - https://www.youtube.com/watch?v=iRCaMnR_bpA
  4. Banglore House Price Predictor - https://www.youtube.com/watch?v=DVxkI1VmpCk
  5. Posture Detection using ML5.js - https://www.youtube.com/watch?v=kRvIcdLhDtU
  6. Laptop Price Predictor - https://www.youtube.com/watch?v=BgpM2IiCH6k
  7. Which bollywood celebrity are you? - https://www.youtube.com/watch?v=X67rclJcIL0
  8. Finding similar GOT characters - https://www.youtube.com/watch?v=ygGknomFEWY
  9. IPL win probability predictor - https://www.youtube.com/watch?v=ygGknomFEWY
  10. T20 score predictor - https://www.youtube.com/watch?v=ygGknomFEWY
  11. Titanic Survivor Prediction - https://www.youtube.com/watch?v=Bnp94fpxZjY
  12. Diabetes Prediction using ML - https://www.youtube.com/watch?v=xUE7SjVx9bQ
  13. Fake news prediction - https://www.youtube.com/watch?v=nacLBdyG6jE
  14. Loan Status Prediction - https://www.youtube.com/watch?v=XckM1pFgZmg
  15. Gold Price Prediction - https://www.youtube.com/watch?v=9ffkBvh8PTQ
  16. Handwriting Classifier - https://www.youtube.com/watch?v=1B3YIkyPNk0
  17. Flight Fare Prediction - https://www.youtube.com/watch?v=y4EMEpEnElQ
  18. Link for 500+ ML+DL projects - https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

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Python
23
star
19

deploy-ml-model-as-android-app

Code to deploy ML model as an Android App
Jupyter Notebook
20
star
20

nlp-web-app

NLP based app using Flask
Python
19
star
21

matrix-linear-transformation-viz

simple viz tools to visualize matrix linear transformation
Python
17
star
22

deep-learning-optimizers

Animation of all major deep learning optimizers
Jupyter Notebook
17
star
23

cricket-score-predictor

A XgBoost based Cricket Score Predictor
Jupyter Notebook
17
star
24

streamlit-basics

A roundup of important streamlit widgets
Python
16
star
25

nlp-lec4-text-classification

text classification code for my YT course on NLP
Jupyter Notebook
16
star
26

posenet-demo-ml5js

A posenet demo built using ml5.js
JavaScript
16
star
27

dialogflow-telegram-chatbot

Backend for a Dialogflow chatbot
Python
15
star
28

ml-model-deployment-aws-ec2

codes related to aws ec2 deployment
Jupyter Notebook
14
star
29

flights-sql-app

A sample python web app using SQL and Streamlit
Python
14
star
30

advanced-web-scraping

HTML
13
star
31

game-of-thrones-personality-matcher

A t-sne implementation on GOT dataset
Jupyter Notebook
13
star
32

vc-emotion-detection

A ml model to detect emotion from text
Python
13
star
33

bagging-ensemble

Codes related to bagging ensemble
Jupyter Notebook
12
star
34

which-bollywood-celebrity-are-you

A streamlit web app which can tell with which bollywood celebrity you face resembles
Python
12
star
35

ml-pipelines-using-dvc

Code of how to build a ml-pipeline using DVC
Python
12
star
36

mlops-mini-project

A small ml project built using mlops
Python
12
star
37

python-gui-tkinter

Python
11
star
38

linear-regression-assumptions

A python code snippet to test assumptions of linear regression
Jupyter Notebook
11
star
39

pandas-io

pandas I/O functions
Jupyter Notebook
10
star
40

aws-demo-app

An application deployed on AWS RDS and Elastic Beanstalk
HTML
10
star
41

game-of-thrones-word2vec

word2vec applied on game of thrones data
Jupyter Notebook
10
star
42

normal-distribution-vs-t-distribution

A streamlit app to show the diff between normal and t distribution
Python
10
star
43

voting-ensemle

Codes related to Voting Ensemble
Jupyter Notebook
9
star
44

ipl-web-app

A small python project built to consume the IPL api
HTML
9
star
45

ipl-api-service

A api service for IPL cricket matches built using Flask
Python
9
star
46

nlpapp

An API based NLP application created using Tkinter and OOP
Python
8
star
47

dtreeviz-demo

Demo of a new awesome decision tree viz library
Jupyter Notebook
8
star
48

python-generators

Demo code for python generators
Jupyter Notebook
8
star
49

z-distribution-confidence-interval

A simple tool to observe relationship between margin of error and confidence level
Python
8
star
50

portfolio-website-demo

code related to my portfolio website
CSS
8
star
51

confidence-interval-viz

A simple streamlit data app to show Confidence Interval
Python
8
star
52

python-iterators-and-iterables

Demo code for python iterators and iterables
Jupyter Notebook
8
star
53

amazon-clone-php-mysql

An amazon clone built using PHP and MySQL
PHP
7
star
54

running-kaggle-dataset-colab-for-deep-learning

code snippet to run kaggle dataset on google colab
Jupyter Notebook
7
star
55

dsmp-2022-23

All the codes and tasks taught in the CampusX Data Science Mentorship Program 2022-23
Jupyter Notebook
7
star
56

gradient-boosting

jupyter notebooks related to gradient boost
Jupyter Notebook
7
star
57

india-data-viz-mini-project

A mini data viz project India census data using plotly and streamlit
Python
7
star
58

new-git-app

A sample git app
Python
6
star
59

pca

Codes related to PCA Lecture
Jupyter Notebook
6
star
60

image-2-recipe-data-collector

Web scraping code to download the dataset
Jupyter Notebook
6
star
61

data-wrangling

Codes related to data wrangling
Jupyter Notebook
6
star
62

tinder-project-python-mysql

A tinder clone made using python and MySQL
Python
6
star
63

decision-trees

Code related to Decision Tree algorithm
Jupyter Notebook
6
star
64

toy-datasets

Toy datasets that can be used to practically implement various data science concepts
6
star
65

mlops-project-2

Personal project
Python
6
star
66

vaex-demo

A code of the Vaex Library for Out-of-core ML
Jupyter Notebook
5
star
67

movie-recommender-system-content-based

A content based movie recommender system
5
star
68

face-detection-using-mtcnn

Demo code for MTCNN face detection
Python
5
star
69

car-dekho-price-predictor

A regression problem based on car dekho dataset
Jupyter Notebook
5
star
70

agglomerative-hierarchical-clustering-demo

Demo code on agglomerative clustering
Jupyter Notebook
5
star
71

statistics

Jupyter notebook related to statistics
Jupyter Notebook
5
star
72

Support-Vector-Machines-SVM-

Codes related to SVM
Jupyter Notebook
5
star
73

sample-project-for-streamlit-deployment

A sample project of no utility
Python
5
star
74

email-spam-classification-flask

Email spam classification
Python
5
star
75

vc-pipeline-emotion-detection

emotion detection
Python
5
star
76

render-demo

temp repo, soon to be deleted
HTML
5
star
77

streamlit-random-forest-classifier

A Streamlit visualization tool for Random Forest
Python
4
star
78

K-Nearest-Neighbors

All the codes and data-sets related to K Nearest Neighbors algorithms
Jupyter Notebook
4
star
79

linear-regression-ols-from-scratch

Python
4
star
80

streamlit-decision-tree-classifier

Python
4
star
81

streamlit-bagging-regressor

Python
4
star
82

mlflow-model-registry-demo

A demo project on mlflow model registry.
Python
4
star
83

dvc-s3-demo

DVC S3 demo
Python
4
star
84

spacy-pos-tagging

sample code on spacy pos tagging
Jupyter Notebook
4
star
85

streamlit-voting-classifier

Streamlit application for Voting classifier
Python
3
star
86

movies-app-jquery

A simple movie app using jQuery which fetches data from the TMDB API
HTML
3
star
87

python-tkinter-api-news-app

This is a code for a api based news application
Python
3
star
88

ipl-score-predictor-keras

code
Jupyter Notebook
3
star
89

streamlit-bagging-classifier

Streamlit app for Bagging Classifier
Python
3
star
90

streamlit-voting-regressor

Python
3
star
91

online-ml-sklearn-demo

A small demo of online learning using sklearn
Jupyter Notebook
3
star
92

dynamic-array-python

This is a code repo in which we will create a dynamic array aka list using Python
Jupyter Notebook
3
star
93

email-spam-classifier-winter-2021

Python
3
star
94

streamlit-regression-trees

Python
3
star
95

unit-convertor

Python
3
star
96

dvc-demo-online

DVC demo
Python
3
star
97

mlops-project-1

Soon to be deleted
Python
3
star
98

streamlit-test-app

a test app for streamlit nothing useful
Python
3
star
99

firebase-todo

A simple todo application with firebase backend
JavaScript
3
star
100

knn-from-scratch

Python
3
star