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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2016.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (xii, 318 pages) : illustrations. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1784392634 9781784392635 1784390844 9781784390846 |
Internformat
MARC
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| 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 |
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| 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 | |
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| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
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| contents | 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 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 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 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 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 |
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| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn946106058 |
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| indexdate | 2025-04-11T08:43:06Z |
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| isbn | 1784392634 9781784392635 1784390844 9781784390846 |
| language | English |
| oclc_num | 946106058 |
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| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xii, 318 pages) : illustrations. |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Bali, Raghav, author. 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 / Raghav Bali, Dipanjan Sarkar. Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully Birmingham, UK : Packt Publishing, 2016. 1 online resource (xii, 318 pages) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Community experience distilled Online resource; title from PDF title page (viewed April 15, 2016). Includes bibliographical references and index. 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. 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 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 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 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 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 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Apprentissage automatique. R (Langage de programmation) Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast Machine learning fast R (Computer program language) fast Sarkar, Dipanjan, author. has work: R machine learning by example (Text) https://id.oclc.org/worldcat/entity/E39PCFyf3pxFJ364WmPCfwGpGd https://id.oclc.org/worldcat/ontology/hasWork Erscheint auch als: Druck-Ausgabe Bali, Raghav. R Machine Learning By Example Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 |
| spellingShingle | Bali, Raghav Sarkar, Dipanjan 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 / Community experience distilled. 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 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 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 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 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 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Apprentissage automatique. R (Langage de programmation) Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast Machine learning fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh97002073 |
| title | 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 / |
| title_alt | Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully |
| title_auth | 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 / |
| title_exact_search | 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 / |
| title_full | 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 / Raghav Bali, Dipanjan Sarkar. |
| title_fullStr | 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 / Raghav Bali, Dipanjan Sarkar. |
| title_full_unstemmed | 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 / Raghav Bali, Dipanjan Sarkar. |
| title_short | R machine learning by example : |
| title_sort | 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 |
| title_sub | understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Apprentissage automatique. R (Langage de programmation) Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast Machine learning fast R (Computer program language) fast |
| topic_facet | Machine learning. R (Computer program language) Data mining. Apprentissage automatique. R (Langage de programmation) Exploration de données (Informatique) COMPUTERS General. Data mining Machine learning |
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