This repository contains My daily notes and study material for the ML,
- Python Basics
- Numpy Basics & Some intro to Classes and Objects
- Linked List
- Binary Tree and assignment
- Probability basics, and assignment Binomial Distribution Formula
- Books folder contains pages text, for important readings
- Added Notebook for distribution until now normal and binomial is covered
- Maximum Likelihood estimation - how it works and how you can use the log likelihood function to find the param and distribution
- PCA
- Devnagiri Digits recoginition (Still requires more regurazation than PCA)
- MNIST Digit Recoginition
- Shark Tank deal prediction using nltk (TFIDF missing will be adding soon)
- Naive Bayes Mushroom classification (Got involved very deep but got very good accuracy)
NOTE:- If you want to run the code on the fly without downloading just hit the binder icon, starting image in the readme.