Machine-Learning-and-Data-Science

Overview

This is a repository which contains all my work and experience with machine learning, AI and data science. I am an aspiring data scientist and have learnt that the only way to learn the techniques and methods of anything is to get your hands dirty by doing lots of projects. This repo is my attempt to learn what I am the most passionate about: AI, ML and Data Science. The repository is structured as follows:

Table of Contents

Learning Resources and Articles

A collection of articles, books and research papers spanning different topics in data science and machine learning.

Implementation of Machine Learning Algorithms

Implementations of major machine learning algorithms using only Numpy and Python - without using any other external libraries like sklearn, tensorflow, pytorch etc...

Implementation of Reinforcement Learning Algorithms

Implementations of major RL algorithms from the book - Reinforcement Learning: An Introduction. Also includes Pytorch implementations of deep RL algorithms like DQN, DDPG, A3C etc...

Machine Learning Competitions

Code for the online ML competitions I participated in. This includes Kaggle competitions, Analytics Vidhya Competitions and some random datasets I worked on.

Statistics-101

Important statistical algorithms implemented in R - Maximum Likelihood Estimator, Bayesian Parameter Estimator, Method of Moments etc..

Image Colorizer using Neural Networks

Using neural networks to colorise grayscale images. The neural network is written using only Numpy and Python.

Probablistic Search and Destroy