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

Lantz, Brett

ISBN 10: 1801071322 / ISBN 13: 9781801071321

Editore: Packt Publishing, 2023

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Conditions of the book: ?Excellent? Clean pages, wihtout any annotations, nor remarks.

A brief description of this 4th edition:

Machine Learning with R, Fourth Edition (the 10th Anniversary Edition) stands out as an accessible and readable guide focused on the practical application of machine learning techniques using the R programming language. Brett Lantz, with his background in sociology and data science, delivers a book that minimizes theoretical complexity while maximizing real-world implementation.

Strengths and Focus

The book's primary strength is its clear, recipe-like approach, guiding readers through the entire machine learning pipeline: from raw data ingestion and cleaning (using modern tools like the tidyverse) to implementation and evaluation. It is highly practical, featuring numerous step-by-step examples across various real-world problems.

Key coverage includes:

Core Algorithms: Comprehensive coverage of classification (k-Nearest Neighbors, Naive Bayes, Decision Trees), regression methods, and clustering (k-means).

Modern Updates: The 4th edition is fully updated to R 4.0.0 and beyond, incorporating newer libraries and modern best practices, including advice on ethical and bias issues.

Advanced Topics: New chapters dive deeper into feature engineering, ensemble methods (like boosting and bagging), and techniques for handling challenging data (big data, high-dimensionality, sparsity). It also provides an introduction to deep learning concepts.

The Audience

This book is excellent for those with a statistical or analytical background who are looking to apply ML using R, even if they have little prior programming experience. It prioritizes the "how" of model building over the dense mathematical "why," making it a superb hands-on resource for rapid skill acquisition.

Codice articolo ABE-1761578173719

Dati bibliografici

• Titolo: 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
• Editore: Packt Publishing
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1801071322
• ISBN 13: 9781801071321
• Rilegatura: Brossura
• Edizione: quarta edizione
• Condizione: ottimo
• Dimensioni: 4,369 L x 19,05 A x 23,495 P centimetri 4,369 centimetri di larghezza per 19,05 centimetri di altezza per 23,495 centimetri di profondità
• Peso: 1284 grammi
• Cataloghi dei venditori:
  • R Language
  • Data science
  • programming languages

Descrizione articolo

Riassunto

Learn how to solve real-world data problems using machine learning and R

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

Key Features

• The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond
• Harness the power of R to build flexible, effective, and transparent machine learning models
• Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz

Book Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.

Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.

You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.

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
12. Advanced Data Preparation
13. Challenging Data – Too Much, Too Little, Too Complex
14. Building Better Learners
15. Making Use of Big Data

Informazioni sull?autore

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.