Thoughtful Machine Learning with Python: A Test-Driven Approach - Tapa blanda

Reseña del editor

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment.

You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for testing machine learning code. The author provides real-world examples to demonstrate the results of using machine-learning code effectively.

Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms.

Biografía del autor

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 dollarportfolio.

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 hisaccomplishments 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.