- "Version Control with Git" by Software Carpentry Foundation
- Git cheat sheet
- GitHub flow guides
- GitHub flavored markdown guides
- Complete list of Github markdown emoji markup
- GitHub Classroom
- GitHub Education - hands-on experience for students, benefits for teachers and more.
- GitHub Pages
- GitHub Skills - "interactive courses for beginners and experts"
You can also download your favorite Octocat stickers.
- "Python Notes for Professionals" by GoalKicker.com
- "A Whirlwind Tour of Python" by Jake VanderPlas
- "Object-oriented Programming in Python for Mathematicians" by David A. Ham
- YouTube Series "Python Programming for Beginners Tutorials" by C.Schafer
- YouTube Series "Pandas Tutotials" by C.Schafer
- YouTube Series "Matplotlib Tutorials" by C.Schafer
- MicroPython
- "Mathematics for Machine Learning" by Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- "Quantitative Economics with Python" by T.J.Sargent and J.Stachurski
Linear Algebra
- "Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares" by S.Boyd and L.Vandenberghe
- YouTube Series "Essence of Linear Algebra" 3Blue1Brown by Grant Sanderson
Calculus
- YouTube "Essence of Calculus" 3Blue1Brown by Grant Sanderson
- "The Matrix Cookbook" by K.B.Petersen and M.S.Pedersen
Probability and Statistics
- "Introduction to Probability for Data Science" by Stanley H. Chan
- "Probabibility and Bayesian Modeling" by J.Albert and J.Hu
- "Probability, Statistics, and Random Processes For Electrical Engineering" by A. Leon-Garcia
- "First Course in Probability" by S.Ross
- "Think Stats" by Allen Downey
- "Think Bayes" by Allen Downey
- "Think DSP" by Allen Downey
- "Seeing Theory"
- MIT OpenCourseWare 6.041 "Probabilistic Systems Analysis and Applied Probability"
- "Foundations of Data Science with Python" by J.M.Shea
- "Python Data Science Handbook" by J.VanderPlas
- "Data Analysis with Python" by FreeCodeCamp.org
- "Pattern Recognition and Machine Learning" by C.Bishop
- "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- "Machine Learning - A First Course for Engineers and Scientists" by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
- "Pattern Recognition" by S.Theodoridis and K.Koutroumbas
- "Pattern Classification" by R.O.Duda, P.E.Hart, D.G.Stork
- "Python Machine Learning" by S.Raschka and V.Mirjalili
- "Machine Learning: A Probabilistic Perspective" by K.P.Murphy
- "Machine Learning: A Constrained-based Approach" by M.Gori
- "Human-in-the-Loop Machine Learning" by R.Monarch
- "Hands-On Machine Learning with Scikit-Learn and Tensorflow" by A.Géron
- "TinyML - Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers" by P.Warden and D.Situnayake
- YouTube "Neural Networks" 3Blue1Brown by Grant Sanderson
- "A Neural Network Playground" by TensorFlow
- "Pen and Paper Exercises in Machine Learning" by Michael U. Gutmann
- "Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI" by Shlomo Kashani and Amir Ivry
- "Introduction to Deep Learning" by E.Charniak
- "Deep Learning" by I.GoodFellow, Y.Bengio and A.Courville
- "Deep Learning with PyTorch" by E.Stevens, L.Antiga, T.Viehmann
- "Dive into Deep Learning" by A.Zhang, Z.C.Lipton, M.Li and A.J.Smola
- "Artificial Intelligence - A Modern Approach" by Stuart Russell and Peter Norvig
- "Reinforcement Learning - An Introduction" by R.Sutton and A.G.Barto
- "Feedback Systems: An Introduction for Scientists and Engineers" by K.J.Åström and R.M.Murray
- "Interpretable Machine Learning - A Guide for Making Black Box Models Explainable" by Christoph Molnar
- "Fairness and Machine Learning - Limitations and Opportunities" by Solon Barocas, Moritz Hardt, Arvind Narayanan