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.
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Think Python - How to Think Like a Computer Scientist, 2012, by Allen B. Downey.
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R for Data Science, 2017, by Hadley Wickham and Garrett Grolemund.
- 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
- 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.
- 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
- Fundamentals of Data Visualization, 2019, by Claus O. Wilke.
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Learning from Data, introductory machine learning online course by Yaser S. Abu-Mostafa from Caltech.
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CS229 - Machine Learning, introduction to machine learning and statistical pattern recognition from Stanford.
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Data Science at Home - A podcast about machine learning, artificial intelligence and algorithms.
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Yannic Kilcher - videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.
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AI Coffee Break with Letitia - Lighthearted bite-sized Machine Learning videos for everyone
- StatQuest - breaks down the major methodologies into easy to understand pieces.
- 3Blue1Brown - some combination of math and entertainment. Difficult problems made simple with great animations.
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arXiv.org > cs > cs.LG - the latest scholarly articles in the field of machine learning (CS).
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arXiv.org > stat > stat.ML - the latest scholarly articles in the field of machine learning (Stat).