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.
01_Gradient_Boosting_Scratch.ipynb This jupyter notebook has implementation of basic gradient boosting algorithm with an intuitive example. Learn about decision tree and intuition behind gradient boositng trees.
02_Collaborative_Filtering.ipynb Builting MovieLens recommendation system with collaborating filtering using PyTorch and fast.ai.
03_Random_Forest_Interpretetion.ipynb How to interpret a seemimngly blackbox algorithm. Feature importance, Tree interpretor and Confidence intervals for predictions.
04_Neural_Net_Scratch.ipynb Using MNSIT data, this notebook has implementation of neural net from scratch using PyTorch.
05_Loss_Functions.ipynb Exploring regression and classification loss functions.
06_NLP_Fastai.ipynb Naive bayes, logistic regression, bag of words on IMDB data.
07_Eigenfaces.ipynb Preprocessing of faces and PCA analysis on the data to recontruct faces and see similarities among differnt faces.