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Tina Huynh
Tina Huynh

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ML Books You Need to Read

Table of Contents

  1. The Big Questions
  2. Machine Learning for Absolute Beginners
  3. Deep Learning with Python
  4. Machine Learning Design Patterns
  5. Machine Learning Yearning
  6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  7. Interpretable Machine Learning
  8. Python Machine Learning
  9. Python for Data Analysis

The Big Questions

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How Do I Get Started with ML?

  1. Adjust your mindset. Yes, you can practice and apply machine learning.
  2. Pick a process.
  3. Pick a tool.
  4. Practice on data sets.
  5. Build a portfolio

What You'll Want to Learn

  1. Calculus
  2. Linear Algebra
  3. Programming
  4. Statistics
  5. Machine Learning (Of course)

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Machine Learning for Absolute Beginners

This is a great book for those who don't have a lot of coding experience or mathematical knowledge. You get a starting point at core algorithms as well as statistical concepts that apply in machine learning.

Deep Learning with Python

The author of this book is the creator of Keras. The book is filled with hands-on examples to help you understand deep learning and machine learning.

Machine Learning Design Patterns

This book in particular was written by three Google engineers and is a great stand-alone book. There is also Introducing MLOps: How to Scale Machine Learning in the Enterprise.

Machine Learning Yearning

The PDF of this book is available right here on GitHub FOR FREE and is a MUST READ! Andrew Ng is a Stanford University professor who co-founded Coursera,, etc.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

If you're going to dive into this book in particular, you'll want to have previous knowledge in Python since it goes into libraries such as Scikit-Learn, Keras and TensorFlow. If you do have the experience in Python, this book is a great resource for going through production-ready Python frameworks.

Interpretable Machine Learning

This book is ABSOLUTELY FREE online right here. The book goes through concepts such as decision trees and linear regression. The book is built from the master branch of the GitHub repo.

Python Machine Learning

This book is NOT for beginners. It goes over the different frameworks, models, and techniques used in machine learning and assumes readers have proficient understanding in both Python and machine learning beforehand.

Python for Data Analysis

Python for Data Analysis is a wonderful first book on machine learning, but does not go into that much depth into the topic. This book goes through how to manipulate, clean, and visualize data.

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Happy coding!

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