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academics

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

deep-learning

deep-learning-in-production

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

face-feature-identification

iwg-web

Work of Institute Wellness Group,IIT Kharagpur

jl-quiz

A Justice League quiz that has four different levels of questions, as you go by the difficulty of the quiz increases. It's a feast for the Justice League fans as I have uploaded some scenes from the Justice League movies . Check out the website and see if you are a true Justice League fan or not

latin-alphabet-recognition-using-convolutional-neural-networks-in-tensorflow

Recognizes Images of Latin Alphabet with up to 89% accuracy. Credits to gregv for his dataset on Kaggle which can be found here: https://www.kaggle.com/gregvial/comnist

loan-prediction-problem

Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

machine-learning

Different Algorithm Implementations have been done like, linear , ridge , lasso regression of which linear regression, ridge regression have been implemented from scratch, naive bayes classification also has been implemented from scratch, decision trees , ensamble classification have also been implemented. Another task on OpenCV of noising and de-noising images has also been done from scratch

open-iit-da

A major record label wants to purchase the rights to a music track. It does not want to encounter any losses with promotion and distribution of the track. It needs to decide on the royalties to be paid to the artists and composers. Objective: You need to predict the popularity of the music tracks based on the features provided in the dataset. The target variable, “popularity”, has 5 categories: ‘Very high’, ‘high’, ‘average’, ‘low’, ‘very low’. The order is in decreasing popularity. For each category, there is initial bid price (for royalties to be paid) and expected revenue collections(in 10k $) Scoring: Based on the predictions, 10000(in 10k $) will be invested to place bids on the 4000 music tracks. The model should generate the highest possible revenue.

snakes

ta_app

task

Sentiment Analysis API

the-sparks-foundation---grips-intern-task-

The video is the recording of three tasks 1) Decision Tree model on Iris Dataset 2)K-means Clustering on Iris Dataset 3)Linear Regression Model for Hours v/s Score of a student

treehouse

zooxy