Main focus of this repo is to have implementation of various ML algoriths from scratch. It will be helpful for someone who wants not only wants to conceptually and intuitively understand various algos, but also learn how to code them.
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Basic Gradient Boosting algorithm from scratch In this jupyter notebook, I have implementation of basic gradient boosting algorithm with an intuitive example. Learn about decision tree and intuition behind gradient boositng trees.
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Neural Net from scratch Using MNSIT data, this notebook has implementation of neural net from scratch using PyTorch.
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Collaborating Filter Builting MovieLens recommendation system with collaborating filtering using PyTorch and fast.ai.
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Intuitive Interpretetion of Random Forests How to interpret a seemimngly blackbox algorithm. Feature importance, Tree interpretor and Confidence intervals for predictions.
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NLP Naive bayes, logistic regression, bag of words on IMDB data.
PS: I am a Data Science student at USF. Most of the content in this repo is motivated from what I study in class.