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Image-Captioning-using-Attention-Mechanism-Local-Attention-and-Global-Attention-
Implemented Image Captioning Model using both Local and Global Attention Techniques and API'fied the model using FLASKNamed-Entity-Recognition-using-ELMo-BiLSTM
Emotion-Recognition-in-Conversation
Here we will predict the emotions from dialogues using Machine Learning and Deep Learning techniquesCalibration-Techniques-in-Machine-Learning
Face-Recognition-in-Live-Stream-using-VGG-16
Build-Train-and-Deploy-ML-Models-using-AWS-Sagemaker
Stock-Market-Prediction-using-LSTM
Predict whether to sell or buy the stocks based on past data trend from NIFTY 50 wiki page web-scraped dataData-Structures-and-Algorithms-Code-Notes
PySpark-in-action
Dealt with Regression and MultiClass Classification problems using PySparkImage-Augmentation---imgaug
Perform Image Augmentation using "imgaug" python libraryAnomalyDetection---Deep-AUTOENCODERS
YOLO-V3-Object-Detection-in-Realtime-Video-Stream
Build-and-Deploy-an-Machine-Learning-Model-using-AWS-and-API-s
Build an API for your Machine Learning application using Flask and Deploy it in AWSSelf-Driving-Car
House-Price-Prediction
Netflix-Movie-Recommendation-System
Automatic-Face-Recognition
Detects that the user is valid or not !Approach-any-NLP-problem
Using state-of-the-art techniques to tackle any NLP problemEntity-Matching
Match names of hotels with good precisionML-Training-Basic-to-Advanced-
Summarizer_Gramformer_Paraphraser_Styleformer
StackOverflow-Tag-Predictor
StackOverflow Tag PredictorDonorsChoose
DonorsChoose.orgGRAKN-KnowledgeGraphs
Amazon-s-Apparel-Recommendation-System
Amazon's Apparel Recommendation SystemTopicModelling_using_LDA_and_Bertopic
KERAS-on-MNIST-dataset
Digit-Recognizer
Recognizes Hand-Written Digits(example: Digit written on a white paper)Sales-Demand-Prediction
simple-dvc-demo
Telecom-Deliquency-Model
Created a delinquency model which can predict in terms of a probability for each loan transaction, whether the customer will be paying back the loaned amount within 5 days of insurance of loan (Label ‘1’ & ’0’)Document-Classification-using-Deep-Learning
To download the dataset : https://www.cs.cmu.edu/~aharley/rvl-cdip/Deploy-ML-Flask-WebApi-using-Kubernetes
Personalised-Cancer-Diagnosis
Personalised Cancer DiagnosisHackerEarth-Challenge-On-the-Plague-Trail
HackerEarth ChallengeDiabetesPrediction_Deployed-Heroku
https://diabetes-specialist.herokuapp.com/Human-Activity-Recognition-Deep-Learning-
Human Activity Recognition using Deep LearningBeer-Rating-Prediction
Build a Machine Learning model which predicts the overall rating of the beer. (“review/overall” column in “train.csv” is your dependent variable.)Local-to-S3-File-Upload-FLASK-WebApp-
TimeSeriesForecasting-ARIMA-SARIMA
Pymongo---MongoDB-with-Python
Deploy-ML-Flask-WebApi-using-Docker
IPL-First-Inning-Score-Predictor
To predict the score range for the first Innings (Regression setup) : https://ipl-1stinnings-score-predictor.herokuapp.com/Covid-19-VizApp
Quora-Question-Pair-Similarity
Quora Question Pair SimilarityLive-Sketch-using-OpenCV
RestAPI-Demo
Type-of-Lentil-and-Nuts---Deep-Learning
Image Classification - Classify the type of Lentils and NutsStock-Price-Prediction-using-LSTM
Music-Generation-using-Deep-Learning
Music Generation using Deep Learningcoursework---diabetes-prediction
YOLOV3-Object-Detection-in-Images
mlops_main
PYCARET-in-action
PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within seconds in your choice of notebook environment .License_plate_recognition_deeplearning-CNN-
Used VGG-16 model architectureLoan-Defaulter-Prediction
Problem Statement : The Bank Indessa has not done well in the last 3 quarters. Their NPAs (Non Performing Assets) have reached all time high. It is starting to lose the confidence of its investors. As a result, it’s stock has fallen by 20% in the previous quarter alone. After careful analysis, it was found that the majority of NPA was contributed by loan defaulters. With the messy data collected over all the years, this bank has decided to use machine learning to figure out a way to find these defaulters and devise a plan to reduce them. This bank uses a pool of investors to sanction their loans. For example: If any customer has applied for a loan of $20000, along with the bank, the investors perform due diligence on the requested loan application. Keep this in mind while understanding data. In this challenge, you will help this bank by predicting the probability that a member will default.SubhamSarkar-PortfolioWebsite
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