Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Minimal and clean examples of machine learning algorithms implementations

A curated list of awesome machine learning interpretability resources.

H2O.ai Machine Learning Interpretability Resources

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from data mining to deep learning.

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

Scipy Cookbook

A collection of commonly used machine learning algorithms implemented in Python/Numpy

Repo for assignments for Georgia Tech's CS 7641 course

Machine learning algorithms from scratch

Machine Learning stuff from CS 4641 at Georgia Tech

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Materials for H2O workshop at eRum 2018 (http://2018.erum.io/)

operations research and supply chain notes

GATech OMSCS Machine Learning Course -- notes and assignments

An AI agent that solves Raven's Progressive Matrices

The Coursera course Coding the Matrix: Linear Algebra through Computer Science Applications by Philip Klein