Course Materials
1. Book:
Must Reads:
- Artificial Intelligence: A Modern Approach by Peter Norvig and Stuart J. Russell
- Deep Learning Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio
- Pattern Recognition and Machine Learning by Christopher Bishop
- Reinforcement Learning: an Introduction by Richard Sutton and Andrew Barto
- Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman
For a Hands-On Approach:
- Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron
2. Mathematics:
All in One Courses and Book:
- Mathematics for Machine Learning by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
- Mathematics for Machine Learning Specialization on Coursera by Imperial College London
- Mathematics for Machine Learning and Data Science Specialization by DeepLearning.AI
Linear Algebra:
Multivariate Calculus:
Probability and Statistics:
- StatQuest with Josh Starmer
- Probability - The Science of Uncertainty and Data on edX by MIT
- Statistics 110: Probability by Joe Blitzstein
- Machine Learning: A Probabilistic Perspective by Kevin P.Murphy
3. Course Lectures:
Stanford:
- CS229: Machine Learning by Andrew Ng, Moses Charikar & Carlos Guestrin
- CS230: Deep Learning by Andrew Ng
- CS231n: Convolutional Neural Networks for Visual Recognition by Fei Fei Li , Andrej Karpathy & Justin Johnson()
- CS224N: Natural Language Processing with Deep Learning by Chris Manning
- CS224W: Machine Learning with Graphs by Jure Leskovec
- CS 11-711: Advanced NLP by Graham Neubig
Other Institutes:
- CS 11-711: Advanced NLP by Graham Neubig
- MIT 6.S191: Introduction to Deep Learning by Alexander Amini
- DS-GA 1008: Deep Learning by Yann LeCun & Alfredo Canziani
4. Machine Learning Engineering Courses and Books:
- Full Stack Deep Learning Course
- Made With ML by Goku Mohandas
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen