Thoughtful Machine Learning
A Test-Driven Approach
Publisher: O'Reilly Media
Final Release Date: September 2014
Pages: 234

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
 
2.0

(based on 2 reviews)

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2.0

Promising, but needs editing

By Dan

from New Haven, CT

About Me Developer

Verified Reviewer

Pros

  • Good Subject
  • Good Topic

Cons

  • Assumes Too Much Backgrou
  • Difficult to understand
  • Too many errors

Best Uses

  • Expert

Comments about oreilly Thoughtful Machine Learning:

This is a review of an Early Release revision.

I love the idea for this book. Using TDD to test your ML model is interesting, and I suspect it will illuminate some practical aspects of using ML.

The text is rough. It reads like the author is used to explaining himself verbally, and hasn't found his written voice yet. Some sentences are incomplete.

I hope to revise my review once the final revision is out.

 
2.0

Severe need for editing

By Disappointed

from San Jose, CA

Verified Buyer

Pros

    Cons

    • Difficult to understand
    • Too many errors

    Best Uses

      Comments about oreilly Thoughtful Machine Learning:

      I get it that this book is in Early Release form, but I am rather shocked that O'Reilly would let this get out the door in this form under any any designation. ("Early Outline" edition?) My hope is that this book will be substantially improved, and eventually I'll find that it has important content that I need to learn. The first couple of chapters? Confused presentation, incorrect similies, and dubious references. "Early Release" should only be an option for proven authors, who can be trusted to provide close-to-publishible quality in early drafts. This is far from publishible. I can't believe O'Reilly would have ever let this into the wild 10 years ago.

      Maybe in 9 months I'll take a look at the latest revision, in hopes that it will by then be worth attempting later chapters.

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      October 2014 (est.)