# Resources

These are starting points rather than a required reading list. Choose a
practical tutorial when you want to build something, or a mathematical text
when you want to understand an assumption more deeply. Package documentation
is listed separately in the [Software Toolkit](../general-ml/foundations/software-toolkit/index.md).

## Practical starting points

- [*Hands-On Machine Learning with Scikit-Learn and PyTorch*](https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/)
  by Aurélien Géron is a broad, implementation-focused introduction aligned
  with this site's PyTorch-first stack.
- [*Deep Learning with Python, Third Edition*](https://www.manning.com/books/deep-learning-with-python-third-edition)
  by François Chollet presents high-level deep-learning workflows with Keras 3
  and modern backends.
- [*An Introduction to Statistical Learning*](https://www.statlearning.com/)
  combines accessible explanations with freely available editions and labs,
  including Python material.
- [*Dive into Deep Learning*](https://d2l.ai/) is an interactive, notebook-based
  introduction with a PyTorch implementation.

## Mathematical and probabilistic foundations

- *The Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani,
  and Jerome Friedman offers a more mathematical treatment of statistical
  learning.
- *Pattern Recognition and Machine Learning* by Christopher Bishop develops
  probabilistic pattern recognition and approximate inference.
- [*Deep Learning*](https://www.deeplearningbook.org/) by Ian Goodfellow,
  Yoshua Bengio, and Aaron Courville is a comprehensive neural-network
  reference.
- [*Probabilistic Machine Learning*](https://probml.github.io/pml-book/) by
  Kevin Murphy provides a modern probabilistic perspective.
- [*Mathematics for Machine Learning*](https://mml-book.github.io/) by Marc
  Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong reviews the linear
  algebra, calculus, probability, and optimization behind common methods.

## Lectures and online courses

- [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction)
  offers a structured, beginner-friendly introduction. Some implementation
  choices differ from this site's PyTorch-first stack, but the core concepts
  transfer.
- [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)
  focuses on neural networks and practical deep-learning methods.
- [fast.ai](https://www.fast.ai/) emphasizes building useful models early and
  understanding the practical decisions behind them.
- [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)
  provides short lessons, visual explanations, and browser-based exercises.
- [MIT 6.036: Introduction to Machine Learning](https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/)
  provides mathematically oriented lectures and course materials.
- [Stanford Machine Learning lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)
  cover a broad set of foundational methods and modeling ideas.
- [Steve Brunton’s Eigensteve channel](https://www.youtube.com/@Eigensteve)
  is a particularly useful source for optimization, dynamical systems, and
  physics-informed machine learning.

```{dropdown} More visual explanations, exercises, and collections
:color: light
:icon: book

- [MLU Explain](https://mlu-explain.github.io/) uses focused visual
  explanations for machine-learning concepts.
- [ML Pen-and-Paper Exercises](https://github.com/michaelgutmann/ml-pen-and-paper-exercises)
  provides mathematical practice without tying the questions to one software
  framework.
- [The Hundred-Page Machine Learning Book](https://themlbook.com/) is a compact
  overview and quick reference.
- [DAIR.AI ML Course Notes](https://github.com/dair-ai/ML-Course-Notes) collects
  notes and links across many machine-learning topics.
- [Machine Learning Guide](https://github.com/mikeroyal/Machine-Learning-Guide)
  is a broad index of tools, courses, books, and communities.
```