Machine Learning: a Concise Introduction

Specifications

Book Description

An introduction to machine learning that covers essential methodologies, techniques, and applications in addition to the topic itself.

The document titled Machine Learning: a Concise Introduction (PDF) provides an in-depth introduction to the methodologies, fundamental principles, and application areas of machine learning. The author, who is an expert in the subject, explains the vocabulary, fundamental ideas, and approaches for addressing applicable issues in classification, clustering, regression, density estimation, and dimension reduction. He is an authority in the field. The bias-variance trade-off and the influence it has on the design of ensemble methods are two of the design considerations that are emphasized while discussing the techniques that underpin them. A better understanding of these concepts leads to applications that are both more successful and adaptable. Methods for risk calculation, optimization, and model selection are included in Machine Learning: a Concise Introduction as well. These are fundamental components of the majority of practical projects.

This extremely useful resource:

• Contains information that is helpful for communicating with customers in an efficient manner
• Includes R source code that demonstrates how to apply and comprehend a large number of the covered techniques.
• Contains a significant number of challenging tasks as an integrated component of the textbook, together with an appendix containing selected answers.
• Explains numerous classification schemes by means of a single, continuously updated example, elaborating on the parallels and contrasts between the various schemes.

Machine Learning: a Concise Introduction is a book that is a part of the well-known Wiley Series in Probability and Statistics. This book provides the reader with the useful information that is required to have a grasp of the methods and applications of machine learning.

PLEASE TAKE NOTICE That the PDF version of Machine Learning: a Concise Introduction by Knox is the only thing that is included in this source. There are no access codes provided.