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內(nèi)容簡介:　　《R語言機器學習（第2版 影印版）》與時俱進。攜新的庫和現(xiàn)代的編程思維為你絲絲入扣地介紹了專業(yè)數(shù)據(jù)科學必不可少的技能。不用再懼怕理論知識。書中提供了編寫算法和處理數(shù)據(jù)所需的關鍵的實用知識，只要有基本的經(jīng)驗就可以了。
　　你可以在書中找到洞悉復雜的數(shù)據(jù)所需的全部分析工具，還能學到如何選擇正確的算法來解決特定的問題。通過與各種真實問題的親密接觸，你將學會如何應用機器學習方法來處理常見的任務，包括分類、預測、市場分析以及聚類。
　　目標讀者可能你對機器學習多少有一點了解，但是從沒用過R語言，或者是知道些R語言，但是沒接觸過機器學習。不管是哪一種情況，《R語言機器學習（第2版 影印版）》都能夠幫助你快速上手。如果熟悉一些編程概念自然是好的。不過并不要求之前有編程經(jīng)驗。
　　你將從《R語言機器學習（第2版 影印版）》中學到什么駕馭R語言的威力，使用真實的數(shù)據(jù)科學應用構建常見的機器學習算法。
　　學習利用R語言技術對待分析數(shù)據(jù)進行清理和預處理并可視化處理結果。
　　了解不同類型的機器學習模型，選擇符合數(shù)據(jù)處理需求的*佳模型，解決數(shù)據(jù)分析難題。
　　使用貝葉斯算法和最近鄰算法分類數(shù)據(jù)。
　　使用R語言預測數(shù)值來構建決策樹、規(guī)則以及支持向量機。
　　使用線性回歸預測數(shù)值，使用神經(jīng)網(wǎng)絡建模數(shù)據(jù)。
　　對機器學習模型性能進行評估和改進。
　　學習專用于文本挖掘、社交網(wǎng)絡數(shù)據(jù)、大數(shù)據(jù)等的機器學習技術。

作者簡介:　　布雷特·蘭茨（Brett Lantz），在應用創(chuàng)新的數(shù)據(jù)方法來理解人類的行為方面有10余年經(jīng)驗。他最初是一名社會學家，在學習一個青少年社交網(wǎng)站分布的大型數(shù)據(jù)庫時，他就開始陶醉于機器學習。從那時起，他致力于移動電話、醫(yī)療賬單數(shù)據(jù)和公益活動等交叉學科的研究。

目錄:Preface
Chapter 1： Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
Machine learning successes
The limits of machine learning
Machine learning ethics
How machines learn
Data storage
Abstraction
Generalization
Evaluation
Machine learning in practice
Types of input data
Types of machine learning algorithms
Matching input data to algorithms
Machine learning with R
Installing R packages
Loading and unloading R packages
Summary

Chapter 2： Managing and Understanding Data
R data structures
Vectors
Factors
Lists
Data frames
Matrixes and arrays
Managing 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 model on the data
Step 4 - evaluating model performance
Step 5 -improving model performance
Transformation - z-score standardization
Testing alternative values of k
Summary

Chapter 4： Probabilistic Learning - Classification Using Naive Bayes
Understanding Naive Bayes
Basic concepts of Bayesian methods
Understanding probability
Understanding joint probability
Computing conditional probability with Bayes' theorem
The Naive Bayes algorithm
Classification with Naive Bayes
The Laplace estimator
Using numeric features with Naive Bayes
Example - filtering mobile phone spam with the
Naive Bayes algorithm
Step 1 - collecting data
Step 2 - exploring and preparing the data
Data preparation - cleaning and standardizing text data
Data preparation - splitting text documents into words
Data preparation - creating training and test datasets
Visualizing text data - word clouds
Data preparation - creating indicator features for frequent words
Step 3 - training a model on the data
Step 4 - evaluating model performance
Step 5 -improving model performance
Summary

Chapter 5： Divide and Conquer - Classification Using Decision Trees and Rules
Chapter 6： Forecasting Numeric Data - Regression Methods
Chapter 7： Black Box Methods - Neural Networks and Support Vector Machines
Chapter 8： Finding Patterns - Market Basket Analysis Using Association Rules
Chapter 9： Finding Groups of Data - Clustering with k-means
Chapter 10： Evaluating Model Performance
Chapter 11： Improving Model Performance
Chapter 12： Specialized Machine Learning Topics
Index