Thoughtful Machine Learning with Python

Matthew Kirk

ISBN 10: 1491924136 / ISBN 13: 9781491924136

Published by O'Reilly Media, US, 2017

About this Item

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 # LU-9781491924136

Bibliographic Details

• Title: Thoughtful Machine Learning with Python
• Publisher: O'Reilly Media, US
• Publication Date: 2017
• Language: English
• ISBN 10: 1491924136
• ISBN 13: 9781491924136
• Binding: Paperback
• Condition: New

About this title

Synopsis

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

About the Author

Matthew Kirk has always been “the math guy” to those that know him best. He started his career as a quantitative financial analyst with Parametric Portfolio. While there, he studied momentum and reversal effects in Emerging Markets and optimized their 30 billion dollar portfolio.

He left the finance industry to build the current version of Wetpaint.com, an entertainment website that is visited by over 10 million unique visitors each month. One of his accomplishments while there was the initial prototype of their patent pending Social Publishing Platform, which optimizes their publication strategy for Facebook posting.

He left Wetpaint to work with a small startup in Kansas City called SocialVolt as their Chief Scientist. While there, he worked on sentiment analysis tools and spam filtering of social media data.

In 2012 he started Modulus 7, which is a data science and startup consulting firm. His clients have included Ritani, The Clymb, Siren, Sqoop, and many others.

Matthew 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 has spoken around the world about using machine learning and data science with Ruby. When he’s not working, he enjoys listening to his 2000+ vinyl record collection on his Thorens TD160 Mk2 turntable.