The Art of Machine Learning: A Hands-On Guide to Machine Learning with R

Learn to expertly apply a range of machine learning methods to real data with this practical guide.

Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math.

As you work through the book, you'll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more.

With the aid of real datasets, you'll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You'll also find expert tips for avoiding common problems, like handling "dirty" or unbalanced data, and how to troubleshoot pitfalls.

You'll also explore:

• How to deal with large datasets and techniques for dimension reduction
• Details on how the Bias-Variance Trade-off plays out in specific ML methods
• Models based on linear relationships, including ridge and LASSO regression
• Real-world image and text classification and how to handle time series data

Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you'll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use.

Requirements: A basic understanding of graphs and charts and familiarity with the R programming language

Author: Norman Matloff
Publisher: No Starch Press
Published: 01/09/2024
Pages: 272
Binding Type: Paperback
ISBN: 9781718502109

About the Author
Norman Matloff is an award-winning teacher at UC Davis, with a PhD in Mathematics from UCLA. He is the author of a number of books in the data science area, and his software and web tutorials are used all over the world. His book, Statistical Regression and Classification: from Linear Models to Machine Learning, was the recipient of the 2017 Ziegel Award, given by the prominent technical journal Technometrics. Matloff is frequently asked to give keynote addresses at data science conferences and he also writes about social issues. He was the recipient of the Distinguished Public Service Award from UC Davis and is also the author of The Art of Debugging with GDB, DDD, and Eclipse and The Art of R Programming (both No Starch Press).