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R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully /
About This BookGet to grips with the concepts of machine learning through exciting real-world examples. Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning. Learn to build your own machine learning system with this example-based practi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2016.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Bali, Raghav, |e author. | |
| 245 | 1 | 0 | |a R machine learning by example : |b understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully / |c Raghav Bali, Dipanjan Sarkar. |
| 246 | 3 | 0 | |a Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2016. | |
| 300 | |a 1 online resource (xii, 318 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from PDF title page (viewed April 15, 2016). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a About This BookGet to grips with the concepts of machine learning through exciting real-world examples. Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning. Learn to build your own machine learning system with this example-based practical guide. Who This Book Is For. If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is the go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge of machine learning would be helpful but is not necessary. What You Will Learn. Utilize the power of R to handle data extraction, manipulation, and exploration techniques. Use R to visualize data spread across multiple dimensions and extract useful features. Explore the underlying mathematical and logical concepts that drive machine learning algorithms. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R and Machine Learning; Delving into the basics of R; Using R as a scientific calculator; Operating on vectors; Special values; Data structures in R; Vectors; Creating vectors; Indexing and naming vectors; Arrays and matrices; Creating arrays and matrices; Names and dimensions; Matrix operations; Lists; Creating and indexing lists; Combining and converting lists; Data frames; Creating data frames; Operating on data frames; Working with functions | |
| 505 | 8 | |a Built-in functionsUser-defined functions; Passing functions as arguments; Controlling code flow; Working with if, if-else, and ifelse; Working with switch; Loops; Advanced constructs; lapply and sapply; apply; tapply; mapply; Next steps with R; Getting help; Handling packages; Machine learning basics; Machine learning -- what does it really mean?; Machine learning -- how is it used in the world?; Types of machine learning algorithms; Supervised machine learning algorithms; Unsupervised machine learning algorithms; Popular machine learning packages in R; Summary | |
| 505 | 8 | |a Chapter 2: Let's Help Machines LearnUnderstanding machine learning; Algorithms in machine learning; Perceptron; Families of algorithms; Supervised learning algorithms; Linear regression; K-Nearest Neighbors (KNN); Unsupervised learning algorithms; Apriori algorithm; K-Means; Summary; Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis; Detecting and predicting trends; Market basket analysis; What does market basket analysis actually mean?; Core concepts and definitions; Techniques used for analysis; Making data driven decisions; Evaluating a product contingency matrix | |
| 505 | 8 | |a Getting the dataAnalyzing and visualizing the data; Global recommendations; Advanced contingency matrices; Frequent itemset generation; Getting started; Data retrieval and transformation; Building an itemset association matrix; Creating a frequent itemsets generation workflow; Detecting shopping trends; Association rule mining; Loading dependencies and data; Exploratory analysis; Detecting and predicting shopping trends; Visualizing association rules; Summary; Chapter 4: Building a Product Recommendation System; Understanding recommendation systems; Issues with recommendation systems | |
| 505 | 8 | |a Collaborative filtersCore concepts and definitions; The collaborative filtering algorithm; Predictions; Recommendations; Similarity; Building a recommender engine; Matrix factorization; Implementation; Result interpretation; Production ready recommender engines; Extract, transform, and analyze; Model preparation and prediction; Model evaluation; Summary; Chapter 5: Credit Risk Detection and Prediction -- Descriptive Analytics; Types of analytics; Our next challenge; What is credit risk?; Getting the data; Data preprocessing; Dealing with missing values; Datatype conversions | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Data mining. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 700 | 1 | |a Sarkar, Dipanjan, |e author. | |
| 776 | 0 | 8 | |i Erscheint auch als: |n Druck-Ausgabe |t Bali, Raghav. R Machine Learning By Example |
| 830 | 0 | |a Community experience distilled. | |
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