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.
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Check this [Springer Book](https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf).
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Great learning guide for new and starting researchers in the Deep neural network (DNN) field.
Check this [Guide at ArXiV](https://arxiv.org/pdf/2004.14545.pdf).
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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)
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A book on Mathematics for Machine Learning that motivates people to learn mathematical concepts.
[Mathematics for Machine Learning](https://mml-book.github.io/)
Examples and tutorials for this book are placed [github](https://github.com/mml-book/mml-book.github.io)
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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/)