Thoughtful Machine Learning with Python : A Test-Driven Approach
Language: English
Published by O'reilly Media Feb 2017, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 77.16
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Add to basketItem description from seller
Neuware - 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.
Seller Inventory # 9781491924136
- Title
- Thoughtful Machine Learning with Python : A Test-Driven Approach
- Author
- Matthew Kirk
- Publisher
- O'reilly Media Feb 2017
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1491924136
- ISBN 13
- 9781491924136
- Item weight
- 376 grams
- Dimensions
- 233x177x17 mm
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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