Machine Learning with R - Third Edition

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Machine Learning with R, 3/e

$1,580.00

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內容描述

Key Features

Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond
Harness the power of R to build flexible, effective, and transparent machine learning models
Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, 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, Third Edition provides a hands-on, 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 uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
What you will learn

Discover the origins of machine learning and how exactly a computer learns by example
Prepare your data for machine learning work with the R programming language
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 ― the basis of deep learning
Avoid bias in machine learning models
Evaluate your models and improve their performance
Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Who this book is for
Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

目錄大綱

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
Improving Model Performance
Specialized Machine Learning Topics

作者介紹

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.