# Machine Learning

- Page: https://shared.btbytes.com/p/machine-learning
- Date: 2018-08-31
- Tags: learning, machine-learning

## Learning

- [How I learn machine learning | ★❤✰ Vicki Boykis ★❤✰](https://vickiboykis.com/2022/11/10/how-i-learn-machine-learning/); 2022.
- [Machine Learning Engineer Roadmap | /home/vigi99](https://viig99.github.io/docs/posts/ml_engineer_guidelines/)
- [ludwigbap/ml learning resources](https://ludwigabap.bearblog.dev/resources-for-ml/)
- [Stanford CS229: Machine Learning, Spring 2022 Notes)](https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf)
	- [Lecture Videos Playlist on Youtube](https://www.youtube.com/playlist?list=PLoROMvodv4rNyWOpJg_Yh4NSqI4Z4vOYy)
[Learning from Data from Caltech](https://home.work.caltech.edu/telecourse.html); [Youtube](https://www.youtube.com/@learningfromdata7836), also uses the eponymous textbook

> When people are getting ready to learn machine learning at MSc level, they don’t learn from a course that says “if you don’t understand it it doesn’t matter”, they learn from this course instead. Abu-Mostafa’s Caltech course is probably the best first course that delivers good understanding of machine learning. [via](https://x.com/predict_addict/status/1701847024889393186)
- [Mathematics for Machine Learning ](https://mml-book.github.io/)  by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.

- [Math Academy - Mathematics for Machine Learning](https://mathacademy.com/courses/mathematics-for-machine-learning).

- [Machine Learning for MacroEconomics](https://www.sas.upenn.edu/~jesusfv/teaching.html) by Jesús Fernández-Villaverde | University of Pennsylvania
- [Home | CS 229S](https://cs229s.stanford.edu/fall2024/); Systems for Machine Learning
- [Math For Machine Learning \[Resources\]](https://lelouch.dev/blog/math-for-machine-learning/) at <https://lelouch.dev>

## Resources

- [franknielsen / Lists of curated books](https://franknielsen.github.io/Books/CuratedBookLists.html)
- [stas00/ml-engineering: Machine Learning Engineering Guides and Tools](https://github.com/stas00/ml-engineering)
- [MAD - Machine learning, Artificial intelligence and Data](https://mattturck.com/landscape/mad2023.pdf); ( PDF chart of the landscape); related reading - [The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape – Matt Turck](http://web.archive.org/web/20250521172525/https://mattturck.com/mad2023/); The [interactive 2024 version](https://mad.firstmark.com)


## Video

- [hu-po - YouTube](https://www.youtube.com/@hu-po); Livestreams on ML papers, Coding, Research.

## Blogs

- [Eugene Yan](https://eugeneyan.com/)
- [ApplyingML - Papers, Guides, and Interviews with ML practitioners](https://applyingml.com/) by Eugene Yan
- [Chip Huyen](https://huyenchip.com/)
- [Leonie Monigatti](https://leoniemonigatti.com/), example see - [37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company](https://leoniemonigatti.com/blog/what_i_learned.html)

## Papers

[Browse the State-of-the-Art in Machine Learning | Papers With Code](https://paperswithcode.com/sota)

[Pen and Paper Exercises in Machine Learning](https://arxiv.org/abs/2206.13446)

> This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.

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See also: [machine-learning-books](https://shared.btbytes.com/p/machine-learning-books)