商品簡介
An accessible guide to machine learning principles for programmers. Features hands-on example projects, real-world case studies, and easy-to-understand explanations.
Practical Machine Learning is a clear, hands-on introduction to machine learning written for programmers -- no extensive background in math required. You'll learn the fundamentals of machine learning and how to use WEKA, a suite of free, open-source tools to build and test "smart" algorithms and incorporate them into your code. The book breaks down the machine learning process, including conducting litmus tests to develop a strategy, preparing your data, preprocessing, and increasing the performance of your algorithm through data normalization. You'll test your new skills with three hands-on experiments: running algorithms that rank customer applications, determine whether a website is malicious, and suggest recommended products. Rather than wallowing in theory, the book is packed with real-world examples, code snippets, and case-studies that put each lesson into practice. Wrapping up with an overview of how to identify Big Data and manage extremely large datasets, Practical Machine Learning is an accessible introduction to this rapidly growing industry, perfect for any programmer looking to apply its principles to their work.
作者簡介
Andrew H. Johnston is an experienced consultant and lead researcher in the machine learning and artificial intelligence domain. He has run a research lab dedicated to using machine learning techniques to solve problems in mobile health, activity recognition, and biometrics, and frequently presents his work at major conferences.
