Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning

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Opis książki : Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of big data and data science. Given the growing prominence of R‚Äîa cross-platform, zero-cost statistical programming environment‚Äîthere has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

Machine Learning with R is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.

How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

Machine Learning with R will provide you with the analytical tools you need to quickly gain insight from complex data.

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O autorze książki

Brett Lantz korzysta z innowacyjnych metod analizy danych, aby lepiej zrozumieć ludzkie zachowanie. Jest z wykształcenia socjologiem i instruktorem DataCamp, prowadzi warsztaty uczenia maszynowego na całym świecie. Interesuje się między innymi zastosowaniami data science w sporcie, grach wideo, pojazdach autonomicznych i nauce języków obcych.

Brett Lantz - pozostałe książki

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To czwarte, zaktualizowane wydanie znakomitego przewodnika poświęconego zastosowaniu uczenia maszynowego do rozwiązywania rzeczywistych problemów w analizie danych. Dzięki książce dowiesz się wszystkiego, co trzeba wiedzieć o wstępnym przetwarzaniu danych, znajdowaniu kluczowych spostrzeżeń, prognozowaniu i wizualizowaniu odkryć. W tym wydaniu dodano kilka nowych rozdziałów dotyczących data science i niektórych trudniejszych zagadnień, takich jak zaawansowane przygotowywanie danych, budowanie lepiej uczących się modeli i praca z big data. Znalazło się tu także omówienie etycznych aspektów uczenia maszynowego i wprowadzenie do uczenia głębokiego. Treść została zaktualizowana do wersji 4.0.0 języka R.
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Machine Learning with R. Expert techniques for predictive modeling - Third Edition

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This second edition provides focused and practical knowledge to help you to build algorithms and crunch your data. You'll learn how to apply machine learning methods to deal with common tasks, discover the analytical tools that you need to gain insights from complex data, and choose the correct algorithm for your specific needs.
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Machine Learning with R. Expert techniques for predictive modeling to solve all your data analysis problems - Second Edition

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With the expert help of Brett Lantz, you’ll learn how to uncover key insights and make new predictions using this hands-on, practical guide to machine learning with R. This 10th Anniversary Edition features an overview of R and plenty of new use cases for advanced users. The book is fully updated to R 4.0.0, with newer and better examples and the most up-to-date R libraries, advice on ethical and bias issues, and new chapters that dive deeper into advanced modeling techniques and methods for using big data in R.
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Machine Learning with R. Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data - Fourth Edition

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R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything
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The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough u
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R: Data Analysis and Visualization. Click here to enter text

Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász

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Tytuł oryginału:: Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.
ISBN Ebooka:: 978-17-821-6215-5, 9781782162155
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Spis treści książki

Machine Learning with R
- Table of Contents
- Machine Learning with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
  - Support files, eBooks, discount offers and more
    - Why Subscribe?
    - Free Access for Packt account holders
- Preface
  - What this book covers
  - What you need for this book
  - Who this book is for
  - Conventions
  - Reader feedback
  - Customer support
    - Downloading the example code
    - Errata
    - Piracy
    - Questions
- 1. Introducing Machine Learning
  - The origins of machine learning
  - Uses and abuses of machine learning
    - Ethical considerations
  - How do machines learn?
    - Abstraction and knowledge representation
    - Generalization
    - Assessing the success of learning
  - Steps to apply machine learning to your data
  - Choosing a machine learning algorithm
    - Thinking about the input data
    - Thinking about types of machine learning algorithms
    - Matching your data to an appropriate algorithm
  - Using R for machine learning
    - Installing and loading R packages
      - Installing an R package
      - Installing a package using the point-and-click interface
      - Loading an R package
  - Summary
- 2. Managing and Understanding Data
  - R data structures
  - Vectors
  - Factors
    - Lists
    - Data frames
    - Matrixes and arrays
  - Managing data with R
    - Saving and loading R data structures
    - Importing and saving data from CSV files
    - Importing data from SQL databases
  - 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
- 3. Lazy Learning Classification Using Nearest Neighbors
  - Understanding classification using nearest neighbors
    - The kNN algorithm
      - Calculating distance
      - Choosing an appropriate k
      - Preparing data for use with kNN
    - Why is the kNN algorithm lazy?
  - Diagnosing breast cancer with the kNN 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
- 4. Probabilistic Learning Classification Using Naive Bayes
  - Understanding naive Bayes
    - Basic concepts of Bayesian methods
      - Probability
      - Joint probability
      - Conditional probability with Bayes theorem
    - The naive Bayes algorithm
      - The naive Bayes classification
      - 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 processing text data for analysis
      - 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
- 5. Divide and Conquer Classification Using Decision Trees and Rules
  - Understanding decision trees
    - Divide and conquer
    - The C5.0 decision tree algorithm
      - Choosing the best split
      - Pruning the decision tree
  - Example identifying risky bank loans using C5.0 decision trees
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
      - Data preparation creating random training and test datasets
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
      - Boosting the accuracy of decision trees
      - Making some mistakes more costly than others
  - Understanding classification rules
    - Separate and conquer
    - The One Rule algorithm
    - The RIPPER algorithm
    - Rules from decision trees
  - Example identifying poisonous mushrooms with rule learners
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
  - Summary
- 6. Forecasting Numeric Data Regression Methods
  - Understanding regression
    - Simple linear regression
    - Ordinary least squares estimation
    - Correlations
    - Multiple linear regression
  - Example predicting medical expenses using linear regression
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
      - Exploring relationships among features the correlation matrix
      - Visualizing relationships among features the scatterplot matrix
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
      - Model specification adding non-linear relationships
      - Transformation converting a numeric variable to a binary indicator
      - Model specification adding interaction effects
      - Putting it all together an improved regression model
  - Understanding regression trees and model trees
    - Adding regression to trees
  - Example estimating the quality of wines with regression trees and model trees
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
    - Step 3 training a model on the data
      - Visualizing decision trees
    - Step 4 evaluating model performance
      - Measuring performance with mean absolute error
    - Step 5 improving model performance
  - Summary
- 7. Black Box Methods Neural Networks and Support Vector Machines
  - Understanding neural networks
    - From biological to artificial neurons
    - Activation functions
    - Network topology
      - The number of layers
      - The direction of information travel
      - The number of nodes in each layer
    - Training neural networks with backpropagation
  - Modeling the strength of concrete with ANNs
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
  - Understanding Support Vector Machines
    - Classification with hyperplanes
    - Finding the maximum margin
      - The case of linearly separable data
      - The case of non-linearly separable data
    - Using kernels for non-linear spaces
  - Performing OCR with SVMs
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
  - Summary
- 8. Finding Patterns Market Basket Analysis Using Association Rules
  - Understanding association rules
    - The Apriori algorithm for association rule learning
      - Measuring rule interest support and confidence
      - Building a set of rules with the Apriori principle
  - Example identifying frequently purchased groceries with association rules
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
      - Data preparation creating a sparse matrix for transaction data
      - Visualizing item support item frequency plots
      - Visualizing transaction data plotting the sparse matrix
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
      - Sorting the set of association rules
      - Taking subsets of association rules
      - Saving association rules to a file or data frame
  - Summary
- 9. Finding Groups of Data Clustering with k-means
  - Understanding clustering
    - Clustering as a machine learning task
    - The k-means algorithm for clustering
      - Using distance to assign and update clusters
      - Choosing the appropriate number of clusters
    - Finding teen market segments using k-means clustering
    - Step 1 collecting data
    - Step 2 exploring and preparing the data
      - Data preparation dummy coding missing values
      - Data preparation imputing missing values
    - Step 3 training a model on the data
    - Step 4 evaluating model performance
    - Step 5 improving model performance
  - Summary
- 10. Evaluating Model Performance
  - Measuring performance for classification
    - Working with classification prediction data in R
    - A closer look at confusion matrices
    - Using confusion matrices to measure performance
    - Beyond accuracy other measures of performance
      - The kappa statistic
      - Sensitivity and specificity
      - Precision and recall
      - The F-measure
    - Visualizing performance tradeoffs
      - ROC curves
  - Estimating future performance
    - The holdout method
    - Cross-validation
    - Bootstrap sampling
  - Summary
- 11. Improving Model Performance
  - Tuning stock models for better performance
    - Using caret for automated parameter tuning
      - Creating a simple tuned model
      - Customizing the tuning process
  - Improving model performance with meta-learning
    - Understanding ensembles
    - Bagging
    - Boosting
    - Random forests
      - Training random forests
      - Evaluating random forest performance
  - Summary
- 12. Specialized Machine Learning Topics
  - Working with specialized data
    - Getting data from the Web with the RCurl package
    - Reading and writing XML with the XML package
    - Reading and writing JSON with the rjson package
    - Reading and writing Microsoft Excel spreadsheets using xlsx
    - Working with bioinformatics data
    - Working with social network data and graph data
  - Improving the performance of R
    - Managing very large datasets
      - Making data frames faster with data.table
      - Creating disk-based data frames with ff
      - Using massive matrices with bigmemory
    - Learning faster with parallel computing
      - Measuring execution time
      - Working in parallel with foreach
      - Using a multitasking operating system with multicore
      - Networking multiple workstations with snow and snowfall
      - Parallel cloud computing with MapReduce and Hadoop
    - GPU computing
    - Deploying optimized learning algorithms
      - Building bigger regression models with biglm
      - Growing bigger and faster random forests with bigrf
      - Training and evaluating models in parallel with caret
  - Summary
- Index