Curated List of Machine Learning Resources

A curated list of machine Learning resource I find very useful. The List is structured into the following topics:

• Programming - resources to get started with the most used programming languages in machine learning.
• Mathematics - great resource to get the mathematical foundation you need for machine learning.
• Statistics - the statistics basic you should know.
• Machine Learning - a lot of interesting resources with focus on machine learning, deep learning and data science.
• Data Visualization - collection of resource which teaches you to visualize your results.

Books

Programming

• Think Python - How to Think Like a Computer Scientist, 2012, by Allen B. Downey.

• R for Data Science, 2017, by Hadley Wickham and Garrett Grolemund.

Mathematics

• A Mathematics Course for Political and Social Research, 2013, by Will H. Moore, David A. Siegel.
• Mathematics for Machine Learning, 2020, by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
• Linear Algebra for Machine Learning: Complete Math Course on YouTube, 2021, by Jon Krohn

Statistics

• An Introduction to Statistical Learning, 2013, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
• The Elements of Statistical Learning, 2009, 2nd edition, by Trevor Hastie Robert Tibshirani, Jerome Friedman.
• Think Stats - Exploratory Data Analysis in Python, 2014, 2nd edition, by Allen B. Downey.
• Think Bayes - Bayesian Statistics Made Simple, 2012, by Allen B. Downey.

Machine Learning

• Probabilistic Machine Learning - An Introduction, 2021, by Kevin Patrick Murphy.
• Introduction to Data Science - Data Analysis and Prediction Algorithms with R, 2021, by Rafael A. Irizarry.
• Deep Learning, 2016, by Ian Goodfellow and Yoshua Bengio and Aaron Courville.
• Dive into Deep Learning, 2020, by Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola
• Deep Learning for Coders with Fastai and PyTorch, 2020, by Sylvain Gugger, Jeremy Howard.
• Automated Machine Learning: Methods, Systems, Challenges}, 2018, by Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin
• Interpretable Machine Learning - A Guide for Making Black Box Models Explainable., 2020, by Christoph Molnar
• Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2023, 3rd ed. draft, by Dan Jurafsky and James H. Martin

Data Visualization

• Fundamentals of Data Visualization, 2019, by Claus O. Wilke.

Online Course (MOOC)

Machine Learning

• Learning from Data, introductory machine learning online course by Yaser S. Abu-Mostafa from Caltech.

• CS229 - Machine Learning, introduction to machine learning and statistical pattern recognition from Stanford.

Podcasts / Videos

Machine Learning

• Data Science at Home - A podcast about machine learning, artificial intelligence and algorithms.

• Yannic Kilcher - videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.

• AI Coffee Break with Letitia - Lighthearted bite-sized Machine Learning videos for everyone

Statistics

• StatQuest - breaks down the major methodologies into easy to understand pieces.

Mathematics

• 3Blue1Brown - some combination of math and entertainment. Difficult problems made simple with great animations.

Papers

Machine Learning

• arXiv.org > cs > cs.LG - the latest scholarly articles in the field of machine learning (CS).

• arXiv.org > stat > stat.ML - the latest scholarly articles in the field of machine learning (Stat).