The Resource 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, Raghave Bali, Dipanjan Sarkar

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, Raghave Bali, Dipanjan Sarkar

Label
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
R machine learning by example
Title remainder
understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
Statement of responsibility
Raghave Bali, Dipanjan Sarkar
Creator
Contributor
Author
Subject
Genre
Language
eng
Member of
Cataloging source
MiAaPQ
http://library.link/vocab/creatorName
Bali, Raghav
Illustrations
illustrations
Index
index present
LC call number
QA76.73.R3
LC item number
B35 2016
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Sarkar, Dipanjan
Series statement
Community experience distilled
http://library.link/vocab/subjectName
R (Computer program language)
Label
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, Raghave Bali, Dipanjan Sarkar
Instantiates
Publication
Copyright
Note
Includes index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
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
  • Data analysis and transformation
Dimensions
unknown
Extent
1 online resource (340 p.)
Form of item
online
Isbn
9781784392635
Media category
computer
Media MARC source
rdamedia
Specific material designation
remote
System control number
  • (CKB)3710000000635652
  • (EBL)4520645
  • (MiAaPQ)EBC4520645
  • (EXLCZ)993710000000635652
Label
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, Raghave Bali, Dipanjan Sarkar
Publication
Copyright
Note
Includes index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
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
  • Data analysis and transformation
Dimensions
unknown
Extent
1 online resource (340 p.)
Form of item
online
Isbn
9781784392635
Media category
computer
Media MARC source
rdamedia
Specific material designation
remote
System control number
  • (CKB)3710000000635652
  • (EBL)4520645
  • (MiAaPQ)EBC4520645
  • (EXLCZ)993710000000635652

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