R语言机器学习 (美)布雷特·兰茨(Brett Lantz) 著 著 azw3 chm 地址 kindle 阿里云 下载 umd pdf

本书与时俱进，携近期新的库和最现代的编程思维为你丝丝入扣的介绍了专业数据科学必不可少的技能。不用再惧怕理论知识，书中提供了编写算法和处理数据所需的最关键的实用知识，只要有最基本的经验就可以了。你可以在书中找到洞悉复杂的数据所需的全部分析工具，还能学到如何选择正确的算法来解决特定的问题。通过与各种真实问题的亲密接触，你将学会如何应用机器学习方法来处理常见的任务，包括分类、预测、市场分析以及聚类。

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

布雷特·兰茨，在应用创新的数据方法来理解人类的行为方面有10余年经验。他最初是一名社会学家，在学习一个青少年社交网站分布的大型数据库时，他就开始陶醉于机器学习。从那时起，他致力于移动电话、医疗账单数据和公益活动等交叉学科的研究，并维护网站，该网站致力于分享有关探寻数据中所蕴含的洞察的知识。

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如果将置信度太低，就可能会被大量不可靠的规则淹没，那么我们如何知道广告预算目标的呢？另一方面，如果将置信规则设置得太高，那么我们将会被显而易见或者不可避免的规则所限制（比如，烟雾探测器总是与电池一起组合购买的事实）。在这种情况下，因为两类商品几乎总是一起购买，所以将烟雾探测器移到电池更近的地方难以再产生额外的收入。

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