This section presents an opinionated list of great machine learning learning resources. Some in PDF, others online is an easy to read format in any browser.
Of course all open.
:::{admonition} Statistics - Crucial for better understanding AI/ML and LLMs! :class: tip, dropdown
Introduction to Modern Statistics :::
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Check this [Springer Book](https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf).
:::{admonition} Deep Learning :class: tip, dropdown
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Explainable Deep Learning: A Field Guide for the Uninitiated.
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Applications of Deep Neural Networks with Keras - with additional material and notebooks :::
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Simple step-by-step walkthroughs to solve common machine learning problems using best practices.
* Rules of ML:Become a better machine learning engineer by following these machine learning best practices used at Google.
* People + AI Guidebook: This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
* Text Classification: This comprehensive guide provides a walkthrough to solving text classification problems using machine learning.
* Good Data Analysis: This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
Check the [guides](https://developers.google.com/machine-learning/guides/).
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Google Machine Learning Education
Learn to build ML products with Google's Machine Learning Courses
[The foundational courses](https://developers.google.com/machine-learning) cover machine learning fundamentals and core concepts.
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Published in 2015, but still a simple and good introduction. Especially for non technical people.
All key concept explained with nice visuals.
Check: [Machines that Learn in the Wild - Machine learning capabilities, limitations and implications](https://media.nesta.org.uk/documents/machines_that_learn_in_the_wild.pdf)
:::{admonition} Mathematics for Machine Learning :class: tip, dropdown
Books on Mathematics for Machine Learning that motivates people to learn mathematical concepts.
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Mathematics for Machine Learning Examples and tutorials for this book are placed github
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Mathematical Introduction to Deep Learning:Methods,Implementations,and Theory (2025)
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The best Scikit-learn Guides around.
* [Scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html)
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Great visuals that help learning and understanding the key ML concepts!
[Interactive learning book that visualizes the fundamental statistical concepts](https://seeing-theory.brown.edu/)
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A practical guide to developing quality predictive models from tabular data.
[Applied Machine Learning for Tabular Data](https://aml4td.org/)
[sources on Github](https://github.com/aml4td/website/)
:::{admonition} Machine Learning :class: tip, dropdown
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