R語言機器學習（第2版 影印版） [Machine Learning with R（Second Edition）] 下載 mobi epub pdf 電子書 2024

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內容簡介

　　《R語言機器學習（第2版 影印版）》與時俱進。攜新的庫和現代的編程思維為你絲絲入扣地介紹瞭專業數據科學必不可少的技能。不用再懼怕理論知識。書中提供瞭編寫算法和處理數據所需的關鍵的實用知識，隻要有基本的經驗就可以瞭。
　　你可以在書中找到洞悉復雜的數據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瞭解，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。不過並不要求之前有編程經驗。
　　你將從《R語言機器學習（第2版 影印版）》中學到什麼駕馭R語言的威力，使用真實的數據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據進行清理和預處理並可視化處理結果。
　　瞭解不同類型的機器學習模型，選擇符閤數據處理需求的*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數據。
　　使用R語言預測數值來構建決策樹、規則以及支持嚮量機。
　　使用綫性迴歸預測數值，使用神經網絡建模數據。
　　對機器學習模型性能進行評估和改進。
　　學習專用於文本挖掘、社交網絡數據、大數據等的機器學習技術。

作者簡介

　　布雷特·蘭茨（Brett Lantz），在應用創新的數據方法來理解人類的行為方麵有10餘年經驗。他最初是一名社會學傢，在學習一個青少年社交網站分布的大型數據庫時，他就開始陶醉於機器學習。從那時起，他緻力於移動電話、醫療賬單數據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
R語言機器學習（第2版 影印版） [Machine Learning with R（Second Edition）] 下載 mobi epub pdf txt 電子書 格式

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評分☆☆☆☆☆

very good book

評分☆☆☆☆☆

very good book

評分☆☆☆☆☆

very good book

評分☆☆☆☆☆

好書

評分☆☆☆☆☆

very good book

評分☆☆☆☆☆

好書

評分☆☆☆☆☆

好書

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