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https://github.com/rueedlinger/ml-resources

A curated list of statistics, data visualization and machine learning resources which in find useful, have read or want to read.
https://github.com/rueedlinger/ml-resources

curated-list data-science data-visualization deep-learning machine-learning statistics

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A curated list of statistics, data visualization and machine learning resources which in find useful, have read or want to read.

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# 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](https://greenteapress.com/wp/think-python/), 2012, by Allen B. Downey.



- [R for Data Science](https://r4ds.had.co.nz/), 2017, by Hadley Wickham and Garrett Grolemund.



## Mathematics



- [A Mathematics Course for Political and Social Research](https://people.duke.edu/~das76/MooSieBook.html), 2013, by Will H. Moore, David A. Siegel.

- [Mathematics for Machine Learning](https://mml-book.github.io/), 2020, by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.

- [Linear Algebra for Machine Learning: Complete Math Course on YouTube](https://www.jonkrohn.com/posts/2021/5/9/linear-algebra-for-machine-learning-complete-math-course-on-youtube), 2021, by Jon Krohn



## Statistics



- [An Introduction to Statistical Learning](https://www.statlearning.com/), 2013, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

- [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/), 2009, 2nd edition, by Trevor Hastie Robert Tibshirani, Jerome Friedman.

- [Think Stats - Exploratory Data Analysis in Python](https://greenteapress.com/wp/think-stats-2e/), 2014, 2nd edition, by Allen B. Downey.

- [Think Bayes - Bayesian Statistics Made Simple](https://greenteapress.com/wp/think-bayes/), 2012, by Allen B. Downey.



## Machine Learning



- [Probabilistic Machine Learning - An Introduction](https://probml.github.io/pml-book/book1.html), 2021, by Kevin Patrick Murphy.

- [Introduction to Data Science - Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/), 2021, by Rafael A. Irizarry.

- [Deep Learning](https://www.deeplearningbook.org/), 2016, by Ian Goodfellow and Yoshua Bengio and Aaron Courville.

- [Dive into Deep Learning](https://d2l.ai/), 2020, by Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola

- [Deep Learning for Coders with Fastai and PyTorch](https://course.fast.ai/), 2020, by Sylvain Gugger, Jeremy Howard.

- [Automated Machine Learning: Methods, Systems, Challenges}](https://www.automl.org/book/), 2018, by Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin

- [Interpretable Machine Learning - A Guide for Making Black Box Models Explainable.](https://christophm.github.io/interpretable-ml-book/), 2020, by Christoph Molnar

- [Speech and Language Processing -  An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition](https://web.stanford.edu/~jurafsky/slp3/), 2023, 3rd ed. draft, by Dan Jurafsky and James H. Martin 



## Data Visualization



- [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/), 2019, by Claus O. Wilke.



# Online Course (MOOC)



## Machine Learning



- [Learning from Data](https://work.caltech.edu/telecourse), introductory machine learning online course by Yaser S. Abu-Mostafa from Caltech. 



- [CS229 - Machine Learning](https://see.stanford.edu/Course/CS229), introduction to machine learning and statistical pattern recognition from Stanford.



# Podcasts / Videos



## Machine Learning



- [Data Science at Home](https://datascienceathome.com) - A podcast about machine learning, artificial intelligence and algorithms.



- [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher/) - 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](https://www.youtube.com/c/AICoffeeBreak) - Lighthearted bite-sized Machine Learning videos for everyone



## Statistics



- [StatQuest](https://www.youtube.com/c/joshstarmer/) - breaks down the major methodologies into easy to understand pieces.



## Mathematics



- [3Blue1Brown](https://www.youtube.com/c/3blue1brown/) - some combination of math and entertainment. Difficult problems made simple with great animations.



# Papers



## Machine Learning



- [arXiv.org > cs > cs.LG](https://arxiv.org/list/cs.LG/recent) - the latest scholarly articles in the field of machine learning (CS).



- [arXiv.org > stat > stat.ML](https://arxiv.org/list/stat.ML/recent) - the latest scholarly articles in the field of machine learning (Stat).