Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

University of Michigan - Ann Arbor

- Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Machine Learning: A Probabilistic Perspective
- Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems by Brett Lantz

Machine Learning Books

 Coursera offers a five course specialization that teaches the foundations of Deep Learning in terms of understanding "how to build neural networks" and learning "how to lead successful machine learning projects". The specialization consists of the following courses:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models


https://www.coursera.org/specializations/deep-learning

Deep Learning Specialization

The Hundred-Page Machine Learning Book is, in reality, about 130 pages. However, it covers a broad range of topics from introductory math (primarily vector and matrix transformations) to building statistical models to the new field of deep learning. It's very dense in content, so it's not like reading a typical 130-page book, but can be finished within a week or two. It's not a textbook so it doesn't have exercises to give you hands on experience, but it's very helpful in glossing a vast scope of machine learning concepts. 

Reference: Burkov, A. (2019). The Hundred-page Machine Learning Book. Leipzig: Andriy Burkov.

PDF Link: [themlbook.com/wiki/doku.php](https://themlbook.com/wiki/doku.php) 

The Hundred-Page Machine Learning Book by Andriy Burkov

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective . MIT Press.

Machine Learning: A Probabilistic Perspective

Lantz, Brett (2015). Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems. Packt Publishing. 

Machine Learning with R (Second Edition)

 Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu (2019)
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
https://www.amazon.com/Effect-Machine-Learning-Springer-Challenges/dp/3030218090

Cause Effect Pairs in Machine Learning

Machine Learning Yearning (Deeplearning.ai)

https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning

Neural Networks and Deep Learning

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning

Structuring Machine Learning Projects

https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning

Convolutional Neural Networks

https://www.coursera.org/learn/nlp-sequence-models

Sequence Models

https://www.coursera.org/professional-certificates/ibm-data-science
Beginner course

IBM Data Science Professional Certificate

https://www.coursera.org/specializations/jhu-data-science
ten course specialization on coursera offered by JHU staff

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