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Vorbestellen	Zweigstelle: 07., Urban-Loritz-Pl. 2a	Standorte: NT.EIA FS.E Matl / College 6c - Englisch	Status: Entliehen	Frist: 18.11.2024	Vorbestellungen: 0

Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice. Additional features:

 

How to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels

A look at typical misconceptions, such as dealing with unbalanced data

Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method

Dozens of illustrative examples involving real datasets of varying size and field of application

Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.

After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.

 

Table of contents:

 

Acknowledgments

 

Introduction

 

PART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS

Chapter 1: Regression Models

Chapter 2: Classification Models

Chapter 3: Bias, Variance, Overfitting, and Cross-Validation

Chapter 4: Dealing with Large Numbers of Features

 

PART II: TREE-BASED METHODS

Chapter 5: A Step Beyond k-NN: Decision Trees

Chapter 6: Tweaking the Trees

Chapter 7: Finding a Good Set of Hyperparameters

 

PART III: METHODS BASED ON LINEAR RELATIONSHIPS

Chapter 8: Parametric Methods

Chapter 9: Cutting Things Down to Size: Regularization

 

PART IV: METHODS BASED ON SEPARATING LINES AND PLANES

Chapter 10: A Boundary Approach: Support Vector Machines

Chapter 11: Linear Models on Steroids: Neural Networks

 

PART V: APPLICATIONS

Chapter 12: Image Classification

Chapter 13: Handling Time Series and Text Data

Appendix A: List of Acronyms and Symbols

Appendix B: Statistics and ML Terminology Correspondence

Appendix C: Matrices, Data Frames, and Factor Conversions

Appendix D: Pitfall: Beware of “p-Hacking”!

 

(Verlagstext)