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- Author: Matthew Kirk
- Publisher: O'Reilly Media
- ISBN-10: 1491924136
- ISBN-13: 9781491924136
- Format: 17.8 x 23.3 x 1.2 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
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
EXTRA 10 % discount with code: EXTRA
The promotion ends in 18d.17:00:56
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Matthew Kirk
- Publisher: O'Reilly Media
- ISBN-10: 1491924136
- ISBN-13: 9781491924136
- Format: 17.8 x 23.3 x 1.2 cm, softcover
- Language: English English
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
Reviews