ML Books and Guides#
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.
AutoML: Methods, Systems, Challenges.
Check this Springer Book.
Explainable Deep Learning: A Field Guide for the Uninitiated.
Great learning guide for new and starting researchers in the Deep neural network (DNN) field.
Check this Guide at ArXiV.
Google Machine Learning Guides
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.
Google Machine Learning Education
Google Machine Learning Education
Learn to build ML products with Google’s Machine Learning Courses
The foundational courses cover machine learning fundamentals and core concepts.
Machines that Learn in the Wild
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
Mathematics for Machine Learning
A book on Mathematics for Machine Learning that motivates people to learn mathematical concepts.
Mathematics for Machine Learning Examples and tutorials for this book are placed github
Seeing Theory, A visual introduction to probability and statistics.
Great visuals that help learning and understanding the key ML concepts!
Interactive learning book that visualizes the fundamental statistical concepts
Applied Machine Learning for Tabular Data
A practical guide to developing quality predictive models from tabular data.