Thoughtful Machine Learning with Python
A Test-Driven Approach
Publisher: O'Reilly Media
Final Release Date: January 2017
Pages: 216

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.

Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:

  • Reference real-world examples to test each algorithm through engaging, hands-on exercises
  • Apply test-driven development (TDD) to write and run tests before you start coding
  • Explore techniques for improving your machine-learning models with data extraction and feature development
  • Watch out for the risks of machine learning, such as underfitting or overfitting data
  • Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
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oreillyThoughtful Machine Learning with Python
 
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This is very alpha

By Phil F

from Baltimore, MD

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Pros

  • Well-written

Cons

  • Too many errors

Best Uses

  • Intermediate

Comments about oreilly Thoughtful Machine Learning with Python:

The book seems reasonably clear and well written. The code, on the other hand needs a lot of work. I'm running Python 3.5 in IntelliJ and cannot get the first example to behave. The data is littered values that should be numbers but instead are "#< Geocoder::Result::Bing:0x007fe9a1b0e478>". There is no entry main() method, so I have to guess at how to run the example. And this is for the first chapter!

I look forward to corrected code so that the book is usable

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