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6 Practical Books for Beginning Machine Learning

Last Updated on November 30, 2017

There are a lot of good books on machine learning, but most people buy the wrong ones.

A question I get asked the most is what books should people buy to get stared in machine learning. My answer to beginners is: “don’t buy textbooks“.

In this post I want to point out a few key books that are aimed at beginners that you should buy (and read!) if you are just starting out.

I am not reviewing these books, if you want reviews, click a link and read the Amazon reviews. I will list a few reasons why I think each is a good book to pick up and read for a beginner.

Data Mining: Practical Machine Learning Tools and Techniques

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I started with this book and it made a big impression on me back in the day.

  • Introduction to applied machine learning (forget the mention of data mining in the title).
  • Focus on the algorithms and on the process of applied machine learning.
  • 100 pages dedicated to the companion platform for applied machine learning called Weka.

If you want to focus on the process and use a mature graphical tool, I highly recommend this book.

Machine Learning an Algorithmic Perspective

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As the title suggests, this book focuses on machine learning algorithms.

  • Focus on machine learning algorithms
  • A little math with lots of examples in Python
  • Sharp focused chapters with references and further exercises

If you’re a programmer and into Python, I highly recommend picking up this book and getting stuck into each example.

Machine Learning in Action

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Another very hands on text with a strong focus on the algorithms.

  • Focus on machine learning algorithms
  • Worked examples in Python (NumPy)
  • Lots of exposition rather than math

There’s a lot of example code, large slabs of it in some places, so I’d suggest that you are competent in Python before giving it a look. 

Programming Collective Intelligence

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This is a very popular book targeted at beginners.

  • Worked examples in Python
  • Larger examples related to the web (rather than toy datasets)
  • Lots of exposition as well as exercises at the end of chapters

Out of the three python-centric books, I’d recommend this one. It is broader and more cohesive than the other two.

Machine Learning for Hackers

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Machine learning is more than just algorithms, there’s a lot of process and analysis work.

  • More time spent on process and analysis
  • Worked problems and examples in R
  • Includes an introduction to R

The data analysis example in the second chapter was amazing. It’s a rare example of how to think about and process a dataset BEFORE you throw algorithms at it. The book is worth it for this example alone.

Applied Predictive Modeling

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Another R book, this one assumes prior knowledge of R, and if you have it, this book is amazing.

  • Treatment on process, but focus on algorithms and their usage
  • Worked examples in R
  • Light on math

This is a big book, but I highly recommend it if you’re ready for it. I’d recommend Machine Learning for Hackers first to get you warmed up.

General Tips

Get the most out of each book you read. If you invested the money to buy it, then invest the time to read it slowly and truly learn something.

  • Pick one book and read it, cover-to-cover.
  • Read with intent, don’t scan.
  • Take notes.
  • Try the exercises, even if you just run the solutions.

About Jason Brownlee

Jason Brownlee, PhD is a machine learning specialist who teaches developers how to get results with modern machine learning methods via hands-on tutorials.

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