Lantz B. - Machine Learning with R [2023, EPUB, ENG] torrent

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Machine Learning with R (Fourth Edition)

Год издания: 2023
Автор: Lantz B.

Издательство: Packt
ISBN: 9781801071321
Язык: Английский

Формат: EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 762

Описание: 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.
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Preface
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black-Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with k-means
Evaluating Model Performance
Being Successful with Machine Learning
Advanced Data Preparation
Challenging Data – Too Much, Too Little, Too Complex
Building Better Learners
Making Use of Big Data
Other Books You May Enjoy
Index
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