Preface
Chapter I-Introducing Machine
The origins of machine learning
Uses and abuses of machine learning
Machine learning successes
The Iimits of machine Iearning
Machine learning ethics
How machines Iearn
Data storage
Abstraction
GeneraIizatiOn
Evaluation
Machine learning in practice
Types ofinput data
Types of machine learning algorithms
Matching input data to algorithms
Machine learning with R
Installing R packages
Loading and unloading R packages
Installing RStudio
Summary
Chapter 2-Managing and Understanding Data
R data structures
Vectors
Factors
Lists
Data frames
Matrices and arrays
Managi ng data with R
Saving，loading，and removing R data structures
Importing and saving data frOm CSV files
Exploring and understanding data
Exploring the structure of data
Exploring numeric variables
Measuring the central tendency-mean and median
Measuring spread—-quartiles and the five-number summary
Visualizing numeric variables-boxplots
Visualizing numeric variables-histograms
Understanding numeric data—uniform and normal distributions
Measuring spread-variance and standard deviation
Exploring categorical variables
Measuring the central tendency-the mode
Exploring relationships between variables
Visualizing relationships-scatterplots
Examining relationships-two--way cross_·tabulations
Summary
Chapter 3-Lazy Learning-Classification Using
Nearest Neighbors
Understanding nearest neighbor classification
The k．NN algorithm
Measuring similarity with distance
Choosing an appropriate k
Preparing data for use with k-NN
Why is the k-NN algorithm lazy?
Example—diagnosing breast cancer with the k-NN algorithm
Step 1-collecting data
Step 2-exploring and preparing the data
Transformation-normalizing numeric data
Data preparation-creating training and test datasets
Step 3-training a modeI on the data
Step 4-evaluating modeI performance
Step 5-improving model performance
Transformation-Z．．score standardization
Testing alternative values of k
Summary
Chapter 4-Probabilistic Learning-—Classification Using
……
Chapter 5-Divide and Conquer-Classification Using Decision
Chapter 6-Forecasting Numeric Data-Regression Methods
Chapter 7-Black Box Methods-Neural Newworks and Support
Chapter 8-Flnding Patterns-Market Basket Analysis Using
Chapter 9-Finding Groups of Data-Clustering with k-means
Chapter 10-Evaluationg Model Perforance
Chapter 11-Improving Model Performance
Chapter 12-Specializad Machine Learning Topics
Other Books You Enjoy
Index

机器学习的核心是将数据转换为可操作的知识。R语言提供了一组强大的机器学习方法，可以轻松且快速地从数据中获取相关信息。
本书第3版提供了具备良好可读性的实践指南，帮助你将机器学习应用于实际问题。无论你是经验丰富的R语言用户还是刚接触这门语言的新手。从Brett Lantz这里都可以学到发掘关键见解、做出新的预测并可视化你的发现所需的一切。
新的这本R语言数据科学的经典之作第3版提供了更新且更好的库、关于机器学习中的伦理和偏差问题的建议以及深度学习的介绍。在数据中寻找强大的新见解，通过R语言揭示机器学习。
你将从本书中学到：
探索机器学习的起源以及计算机究竟是如何通过实例进行学习的；
使用R语言为机器学习工作准备数据；
使用最近邻居和贝叶斯方法对重要结果进行分类；
使用决策树、规则和支持向量机预测未来事件；
使用回归方法预测数字数据并估算财务价值；
用人工神经网络——深度学习的基础来为复杂过程建模；
避免机器学习模型中的偏差；
评估模型并提高其性能；
将R连接到SQL数据库以及新兴大数据技术，例如Spark、H2O和TensorFlow。