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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 /
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book Get to grips with the concepts of machine learning through exciting real-world examples Visualize and solve complex problems by using po...
| 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 (Requiere registro previo con correo institucional) |
MARC
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| 003 | OCoLC | ||
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| 008 | 160419s2016 enka o 001 0 eng d | ||
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| 020 | |a 9781784392635 | ||
| 020 | |a 1784392634 | ||
| 020 | |z 9781784390846 | ||
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| 020 | |a 9781784390846 | ||
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| 035 | |a (OCoLC)946944179 | ||
| 037 | |a CL0500000733 |b Safari Books Online | ||
| 050 | 4 | |a Q325.5 | |
| 082 | 0 | 4 | |a 006.31 |q OCoLC |2 23/eng/20230216 |
| 049 | |a UAMI | ||
| 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. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2016. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 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 | |
| 588 | |a Description based on online resource; title from cover page (Safari, viewed April 15, 2016). | ||
| 500 | |a Includes index. | ||
| 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 | |
| 520 | |a Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book Get 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 a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in 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 Dive deep into the world of analytics to predict situations correctly Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Write reusable code and build complete machine learning systems from the ground up Solve interesting real-world problems using machine learning and R as the journey unfolds Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science In Detail Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of ma... | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Sarkar, Dipanjan, |e author. | |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781784390846/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||