Thoughtful Machine Learning with Python

Matthew Kirk

ISBN 10: 1491924136 / ISBN 13: 9781491924136

Verlag: O'Reilly Media, US, 2017

Beschreibung

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.

Bestandsnummer des Verkäufers LU-9781491924136

Bibliografische Details

• Titel: Thoughtful Machine Learning with Python
• Verlag: O'Reilly Media, US
• Erscheinungsjahr: 2017
• Sprache: Englisch
• ISBN-10: 1491924136
• ISBN-13: 9781491924136
• Einband: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

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

Über die Autorin bzw. den Autor

Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He is also studying for his M.S. in Computer Science at the Georgia Institute of Technology. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby