You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781801071321

Length 762 pages

Edition 4th Edition

Languages

Tools

H2O

Concepts

Big Data

Author (1):

Brett Lantz

View More author details

Table of Contents (18) Chapters

Preface

1. Introducing Machine Learning

2. Managing and Understanding Data FREE CHAPTER

3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules

6. Forecasting Numeric Data – Regression Methods

7. Black-Box Methods – Neural Networks and Support Vector Machines

8. Finding Patterns – Market Basket Analysis Using Association Rules

9. Finding Groups of Data – Clustering with k-means

10. Evaluating Model Performance

11. Being Successful with Machine Learning

12. Advanced Data Preparation

13. Challenging Data – Too Much, Too Little, Too Complex

14. Building Better Learners

15. Making Use of Big Data

16. Other Books You May Enjoy

17. Index

Being Successful with Machine Learning

An all-too-common problem in the field of machine learning occurs when students, with fresh excitement from learning the methods, struggle to apply what they’ve learned to real-world projects. Much as the beauty of a forest trail feels sinister in the darkness of night, code and methods that initially seemed straightforward feel daunting in the absence of a step-by-step roadmap. Without such a guide, the learning curve feels so much steeper and pitfalls appear deeper.

It is discouraging to think about the countless students that have been turned away from machine learning, due to the chasm between machine learning in theory and practice. Having worked in the field for over a decade, and having trained, interviewed, hired, and supervised numerous new practitioners, I have seen the challenges of this catch-22 firsthand. It is seemingly a paradox: gaining real-world experience in machine learning seems impossible without first having...

The rest of the chapter is locked

You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Table of Contents (18) Chapters

Being Successful with Machine Learning

Authors (1)

Personalised recommendations for you

You're reading from Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Table of Contents (18) Chapters

Being Successful with Machine Learning

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you