Machine learning with R : expert techniques for predictive modeling to solve all your data analysis problems

Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior e...

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Bibliographic Details
Main Author: Lantz, Brett, (Author)
Format: eBook
Language: English
Published: Birmingham : Packt Publishing, [2015]
Edition: Second edition.
Series: Community experience distilled.
Subjects:
Machine learning > Statistical methods.
R (Computer program language)
elektronické knihy
electronic books
ISBN: 1784394521
9781784394523
1784393908
9781784393908
Physical Description: 1 online resource

Cover

Table of contents

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035 |a (OCoLC)918590406  |z (OCoLC)916529602  |z (OCoLC)937787253 
100 1 |a Lantz, Brett,  |e author. 
245 1 0 |a Machine learning with R :  |b expert techniques for predictive modeling to solve all your data analysis problems /  |c Brett Lantz. 
250 |a Second edition. 
260 |a Birmingham :  |b Packt Publishing,  |c [2015] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Community experience distilled 
500 |a Includes index. 
505 0 |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; 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. 
505 8 |a Chapter 2: Managing and Understanding DataR 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. 
505 8 |a Measuring spread -- variance and standard deviationExploring 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. 
505 8 |a Step 1 -- collecting dataStep 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. 
505 8 |a Computing conditional probability with Bayes' theoremThe 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. 
505 8 |a Step 3 -- training a model on the data. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Machine learning  |x Statistical methods. 
650 0 |a R (Computer program language) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Erscheint auch als:  |n Druck-Ausgabe  |t Lantz, Brett. Machine Learning with R 
830 0 |a Community experience distilled. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMLREDHB3/machine-learning-with?kpromoter=marc  |y Full text 

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