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, 4th Edition

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
Author: Brett Lantz (Author)
Publisher finelybook 出版社:‏ Packt Publishing
Edition 版本:‏ 4th ed.
Publication Date 出版日期:‏ 2023-05-29
Language 语言: English
Print Length 页数: 762 pages
ISBN-10: 1801071322
ISBN-13: 9781801071321

Book Description

Use R and tidyverse to prepare, clean, import, visualize, transform, program, communicate, predict and model data

No R experience is required, although prior exposure to statistics and programming is helpful

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Get to grips with the tidyverse, challenging data, and big data
• Create clear and concise data and model visualizations that effectively communicate results to stakeholders
• Solve a variety of problems using regression, ensemble methods, clustering, deep learning, probabilistic models, and more

Book Description

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.

Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.

With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.

Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.

Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.

What you will learn

• Learn the end-to-end process of machine learning from raw data to implementation
• Classify important outcomes using nearest neighbor and Bayesian methods
• Predict future events using decision trees, rules, and support vector machines
• Forecast numeric data and estimate financial values using regression methods
• Model complex processes with artificial neural networks
• Prepare, transform, and clean data using the tidyverse
• Evaluate your models and improve their performance
• Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow

Who this book is for

This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.

Table of Contents

1. Introducing Machine Learning
2. Managing and Understanding Data
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

(N.B. Please use the Look Inside option to see further chapters)