R語(yǔ)言機器學(xué)習（第2版 影印版）簡(jiǎn)介，目錄書(shū)摘

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內容簡(jiǎn)介:　　《R語(yǔ)言機器學(xué)習（第2版 影印版）》與時(shí)俱進(jìn)。攜新的庫和現代的編程思維為你絲絲入扣地介紹了專(zhuān)業(yè)數據科學(xué)必不可少的技能。不用再懼怕理論知識。書(shū)中提供了編寫(xiě)算法和處理數據所需的關(guān)鍵的實(shí)用知識，只要有基本的經(jīng)驗就可以了。
　　你可以在書(shū)中找到洞悉復雜的數據所需的全部分析工具，還能學(xué)到如何選擇正確的算法來(lái)解決特定的問(wèn)題。通過(guò)與各種真實(shí)問(wèn)題的親密接觸，你將學(xué)會(huì )如何應用機器學(xué)習方法來(lái)處理常見(jiàn)的任務(wù)，包括分類(lèi)、預測、市場(chǎng)分析以及聚類(lèi)。
　　目標讀者可能你對機器學(xué)習多少有一點(diǎn)了解，但是從沒(méi)用過(guò)R語(yǔ)言，或者是知道些R語(yǔ)言，但是沒(méi)接觸過(guò)機器學(xué)習。不管是哪一種情況，《R語(yǔ)言機器學(xué)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(guò)并不要求之前有編程經(jīng)驗。
　　你將從《R語(yǔ)言機器學(xué)習（第2版 影印版）》中學(xué)到什么駕馭R語(yǔ)言的威力，使用真實(shí)的數據科學(xué)應用構建常見(jiàn)的機器學(xué)習算法。
　　學(xué)習利用R語(yǔ)言技術(shù)對待分析數據進(jìn)行清理和預處理并可視化處理結果。
　　了解不同類(lèi)型的機器學(xué)習模型，選擇符合數據處理需求的*佳模型，解決數據分析難題。
　　使用貝葉斯算法和最近鄰算法分類(lèi)數據。
　　使用R語(yǔ)言預測數值來(lái)構建決策樹(shù)、規則以及支持向量機。
　　使用線(xiàn)性回歸預測數值，使用神經(jīng)網(wǎng)絡(luò )建模數據。
　　對機器學(xué)習模型性能進(jìn)行評估和改進(jìn)。
　　學(xué)習專(zhuān)用于文本挖掘、社交網(wǎng)絡(luò )數據、大數據等的機器學(xué)習技術(shù)。

作者簡(jiǎn)介:　　布雷特·蘭茨（Brett Lantz），在應用創(chuàng )新的數據方法來(lái)理解人類(lèi)的行為方面有10余年經(jīng)驗。他最初是一名社會(huì )學(xué)家，在學(xué)習一個(gè)青少年社交網(wǎng)站分布的大型數據庫時(shí)，他就開(kāi)始陶醉于機器學(xué)習。從那時(shí)起，他致力于移動(dòng)電話(huà)、醫療賬單數據和公益活動(dòng)等交叉學(xué)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