Most popular beginner ML books in 2022

These are the top 6 best seller machine learning books on Amazon, but are they worth it?


Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron

  • Published in 2019
  • Currently the best sell on Amazon (2000+ reviews)
  • Aurelien is a former lead of the YouTube video classification team.
  • A hands-on book to practice in Python (Scikit-Learn and Tensorflow/Keras)
  • Covers everything from traditional to modern machine learning.
    • First half is on the fundamental and traditional ML:
    • Second half is focused on Deep Neural Network
      • CNN
      • RNN
      • Attention model
      • GAN
      • Reinforcement learning
  • It’s also system focus with technique in building and deploying ML system at scale.
  • It’s currently priced at $28.50, which is very reasonable.

The Hundred-Page Machine Learning Book by Andrew Burkov

  • Published in 2019
  • With 700+ reviews, it has quite a catchy title, but it would not really “teach” ML if you don’t already know these concepts.
  • This book can be treated as a quite overview to learn about the various models and what they are.
  • It also reminds you of these models if you forgotten about them.
  • It is a friendly introduction of the subject but it’s quite shallow in many aspects that you might think you are learning it but you are really just learning “about it”.
  • It’s currently priced at $39.95, which is not that cheap considering it only has 137 pages.

Deep Learning by Ian Goodfellow

  • Published in 2016
  • With 1500+ reviews, it’s regarded as a Bible in deep learning because of the author.
  • This book is a bit on the more serious side of ML research and more experienced ML practitioners.
  • It’s not the most friendly book to read but it is more of an inspirational reading because it’s coming from someone who is very successful in applying ML to the real world.
  • This book is not quite hands-on either but more on theory side.
  • Priced at $45, I would not recommend it as your first ML or Deep Learning book out of your pocket, but it’s a good book to keep around if you are a ML practitioner.

Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobald

  • Published in 2018
  • With 900+ reviews, this book is another very friendly read for beginners.
  • While trying to explain the concept in “plain English”, it could be a hit or miss in conveying the actual intuition behind these models.
  • It would a quick read for you to tackle in a few days and provide a good high level introduction.
  • It would be the right level for some beginners but not much if you want some depth or already know the subject.
  • Given its price at only $16.90, this book could be a fine value to keep you interested in reading about ML and not feel overwhelmed.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

  • Published in 2017
  • With 1200+ reviews, this book might be just what you need to get into data science.
  • Practical hands-on fluency is often overlooked by candidates. Many jobs in the market with the Machine Learning title is more for data analyst to som extend. While it might be tough to get a job in ML as a beginner today, it would be actually much easier to start in the data analyst path.
  • This book is exactly the practical knowledge you need to “play with data” as a data scientist.
  • This book gives you the skills to get very hands-on to increase your effectiveness when working on most data science projects.
  • The regular price is a bit high at $59.99, but it appears it’s on sale at the momement for around $28. I would recommend this book to people who
    • has some background in statistics but trying to get into ML
    • trying to do a career switch and attending bootcamp, but want to get more comfortable with data.

Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C Muller

  • Published in 2016
  • This book is another tool book on this list.
  • It gives an introduction to using python and the various library and tools.
  • It basically walks thru all the common models that are out there in scikit-learn.
  • It’s a slightly older book than the other ones. It’s also a less interesting read. All the information in the book is solid however.
  • At $37, this book is an average value for data scientist.

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