Machine Learning with R - Fourth Edition : Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Brett Lantz

ISBN 10: 1801071322 / ISBN 13: 9781801071321

Edité par Packt Publishing, 2023

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - Use R and tidyverse to prepare, clean, import, visualize, transform, program, communicate, predict and model dataNo R experience is required, although prior exposure to statistics and programming is helpfulPurchase of the print or Kindle book includes a free Elektronisches Buch 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 moreBook 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 TensorFlowWho 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.

N° de réf. du vendeur 9781801071321

Détails bibliographiques

• Titre: Machine Learning with R - Fourth Edition : Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
• Éditeur: Packt Publishing
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 1801071322
• ISBN à 13 chiffres: 9781801071321
• Reliure: Taschenbuch
• Édition: 4ème Édition
• État de l'article: Neu
• Dimensions: 235 L x 191 H x 41 P (en millimètres) 235 millimètres de largeur par 191 millimètres de hauteur par 41 millimètres de profondeur
• Poids: 1395 grammes

A propos de ce titre

Synopsis

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

À propos de l?auteur

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.