Rewcifer

ML-Notebooks

:fire: Machine Learning Notebooks

ML-YouTube-Courses

📺 Discover the latest machine learning / AI courses on YouTube.

ML-Course-Notes

🎓 Sharing machine learning course / lecture notes.

Bayes-R-JAGS-intro

An introduction to hierarchical Bayesian modelling with R, JAGS and STAN

sql-style-guide

An opinionated guide for writing clean, maintainable SQL.

reader

A Python feed reader library.

skflow

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

intro-to-python

DDL Intro to Python iPython Notebook

machine-learning

Code & Data for Introduction to Machine Learning with Scikit-Learn

shap-analysis-guide

How to Interpret SHAP Analyses: A Non-Technical Guide

shap-clustering

How to use SHAP values for better cluster analysis

The-Python-Graph-Gallery

A website displaying hundreds of charts made with Python

30-Days-Of-Python

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

my_notes

My small cheatsheets for data science, ML, computer science and more.

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

data-mining

data-science-from-scratch

code for Data Science From Scratch book

fastai

The fastai deep learning library

Deep-Reinforcement-Learning

DRL university course lecture notes & exercises

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ml-cookbook

Toolbox with machine learning algorithms and methods.

themlsbook

This repository is a supplement to the 'Machine Learning Simplified: A Gentle Introduction to Supervised Learning' book.

probability_cheatsheet

A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.

INSEADAnalytics

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Python

All Algorithms implemented in Python

DBDA-python

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

ISLR-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

ISLR

Student Solutions to An Introduction to Statistical Learning with Applications in R

ISL-python

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.