●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 datauniform 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 Neiors
Understanding nearest neior 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?
Examplediagnosing 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-Speizad Machine Learning Topics
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Index
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