study resources
this page is being updated actively with new resources for machine learning ...
This page compiles a collection of high-quality and free resources on machine learning that I have curated from diverse sources. It is my pleasure to share these resources with you, as they can offer valuable insights to anyone interested in this intriguing field. It is important to note that I am NOT hosting any of the content on this page on my own servers and all of the content is hosted on their original servers. Whether you are a novice or an experienced data scientist, I hope you will discover valuable materials here. The list originally started as a GitHub repository , but I have developed this page to replace it to enhance readability. Please check back regularly for newly added content. Moreover, if you have any suggestions for new resources, please do not hesitate to contact me or send them through the Google Form created for this purpose. Let us explore the realm of machine learning together!
This page has been divided into the following sections: Books , Courses, and GitHub Repositories/Blogs (coming soon).
Books
This section includes books that cover a wide range of topics in machine learning. Some of these books are introductory, while others are more advanced. I have also included books that are not directly related to machine learning but are still relevant to the field. I have organized the books by their publication year, from newest to oldest. I have also included the author(s) of each book, as well as the edition number, if applicable. I have also included the front cover of each book, if available.
Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems
Machine Learning in Production: From Models to Products
Foundations of Computer Vision
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications
Deep Learning: Foundations and Concepts
Bayesian Optimization Book
Mathematical Analysis of Machine Learning Algorithms
An introduction to Optimization on Smooth Manifolds
Introduction to Probability for Computing
Linear Algebra Done Right
Machine Learning for Data Streams with Practical Examples in MOA
Automata Theory: An Algorithmic Approach
Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks
Information Theory: From Coding to Learning
Telling Stories with Data: With Applications in R
Bayesian models of perception and action: An introduction
The Little Book of Deep Learning
Understanding Deep Learning
Probabilistic Machine Learning: Advanced Topics
Distributional Reinforcement Learning
Fairness and Machine Learning: Limitations and Opportunities
Learning Theory from First Principles
Machine learning with neural networks
Patterns, Predictions, and Actions: A story about machine learning
Optimization for Modern Data Analysis
Random Matrix Methods for Machine Learning
Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning
Graph Neural Networks: Foundations, Frontiers, and Applicationsg
Reinforcement Learning: Theory and Algorithms
Introduction to Online Convex Optimization
Machine Learning - A First Course for Engineers and Scientists
Large-Scale Convex Optimization
Algorithms for Decision Making
The Principles of Deep Learning Theory
Hands-On Data Visualization
Probabilistic Numerics
Model-based Machine Learning
Probabilistic Machine Learning: An Introduction
Speech and Language Processing
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
Explanatory Model Analysis
Machine Learning Interviews
Learning Statistical Models Through Simulation in R
Deep Learning Interviews
Data Science at the Command Line
Deep Learning on Graphs
Bayes Rules!
Physics-based Deep Learning Book
Introduction to Probability for Data Science
Theory of Computation
Machine Learning and Big Data
Think Bayes
Computer Vision: Algorithms and Applications
An Introduction to Statistical Learning
Dive into Deep Learning
Bandit Algorithms
Machine Learning from Scratch
Mathematics for Machine Learning
Bayesian Reasoning and Machine Learning
Linear Algebra
Automated Machine Learning: Methods, Systems, Challenges
Introduction to Probability
Algorithmic Aspects of Machine Learning
Adversarial Robustness - Theory and Practice
Foundations of Machine Learning
Reinforcement Learning: An Introduction
Elements of Statistical Learning
A Course in Machine Learning
Computer Age Statistical Inference: Algorithms, Evidence and Data Science
Python Data Science Handbook
Deep Learning
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
Neural Networks and Deep Learning
Statistical inference for data science
Statistical Learning with Sparsity: The Lasso and Generalizations
Neural Network Design
Algorithmic Aspects of Machine Learning
Understanding Machine Learning: From Theory to Algorithms
Think Stats
The Matrix Cookbook
Algorithms for Reinforcement Learning
Convex Optimization
Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
A Brief Introduction to Neural Networks
Gaussian Processes for Machine Learning
Pattern Recognition and Machine Learning
Information Theory, Inference, and Learning Algorithms
Machine Learning
Courses
This section includes courses that cover a wide range of topics in machine learning. Similar to the books section, some of these courses are introductory, while others are more advanced. I have organized the courses by their publication year, from newest to oldest. This section is still under construction, and I will move more courses from my GitHub repository to this page soon.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan
- Instructor(s): Richard McElreath
- Year: 2023
Stat 156 / 256: Causal Inference
- Instructor(s): Prof. Peng Ding
- Year: 2023
- Institute: UC Berkeley
Advanced Latent Variable Modeling
- Instructor(s): Steven Andrew Culpepper
- Year: 2023
- Institute: University of Illinois Urbana-Champaign
Multi Modal Machine Learning
- Instructor(s): Louis-Philippe Morency, and Paul Liang
- Year: 2022
- Institute: Carnegie Mellon University
Desription: This revised version of CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification. This revised course is based on the new taxonomy introduced in this survey paper: https://arxiv.org/abs/2209.03430
CS 224W: Machine Learning with Graphs
- Instructor(s): Jure Leskovec
- Year: 2021
- Institute: Stanford
CS25: Transformers United
- Instructor(s): Div Garg, Steven Feng, and Rylan Schaeffer
- Year: 2021
- Institute: Stanford
CS W182 / 282A: Designing, Visualizing, and Understanding Deep Neural Networks
- Instructor(s): Brandon Trabucco
- Year: 2021
- Institute: Robotic AI & Learning Lab at UC Berkeley
Introduction to Causal Inference
- Instructor(s): Brady Neal
- Year: 2020
6.S897: Machine Learning For Healthcare
- Instructor(s): Prof. Peter Szolovits, and Prof. David Sontag
- Year: 2019
- Institute: MIT