Thoughtful Machine Learning
A Test-Driven Approach
Publisher: O'Reilly Media
Final Release Date: September 2014
Pages: 236

Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.

Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start.

  • Apply TDD to write and run tests before you start coding
  • Learn the best uses and tradeoffs of eight machine learning algorithms
  • Use real-world examples to test each algorithm through engaging, hands-on exercises
  • Understand the similarities between TDD and the scientific method for validating solutions
  • Be aware of the risks of machine learning, such as underfitting and overfitting data
  • Explore techniques for improving your machine-learning models or data extraction
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oreillyThoughtful Machine Learning

(based on 2 reviews)

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(3 of 6 customers found this review helpful)



By Adam

from Toronto, Ontario

About Me Developer, Maker


  • Decent Overview
  • Many Algorithms Covered


  • Not comprehensive enough

Best Uses

  • Novice

Comments about oreilly Thoughtful Machine Learning:

I was expecting this book to provide a brief overview of the topic of machine learning and dive more deeply into several approaches.

What I found is:
- Provides a decent conceptual overview of the algorithms, which is excellent
- The deeper dive into the mathematics was frustrating. I do not find math difficult, however the author will often skips important steps in his explanation of the underlying mathematics and algorithms
- Code examples are subpar, they deal too much in the weeds and would have benefited greatly by having a few more abstractions. It introduces an unnecessary burden on the reader

(5 of 5 customers found this review helpful)


Great if you're new to ML or old hat

By Ben B.

from Washington, D.C.

About Me Data Scientist, Developer

Verified Reviewer


  • Concise
  • Helpful examples


    Best Uses

    • Novice
    • Student

    Comments about oreilly Thoughtful Machine Learning:

    I am an academic who studies machine learning and a Python programmer. For those two reasons, I thought that Thoughtful Machine Learning wouldn't be right for me. However, I had another friend who was interested into getting into machine learning - so we decided we'd read through the book together, one chapter at a time.

    That was a great idea.

    This book is a short read, and a chapter a day is very easily digestible. With that chapter you get to explore ML topics from the standard Naive Bayes to things that aren't typically included in an entry level ML book like SVMs and HMMs. Matt does a great job of creating clear examples and uses non-academic language to explain things. His code doesn't rely on libraries and demonstrates the effectiveness of these techniques.

    What that means is that if you're new to ML - this is a good way to get started into it. If you're old hat - it's a fresh perspective and a way to learn how to discuss these topics in fresh manner.

    I am also a practitioner, and I thought it was a very novel thesis to include TDD with ML.

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