R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully, Raghave Bali, Dipanjan Sarkar
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The instance R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully, Raghave Bali, Dipanjan Sarkar represents a material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries. This resource is a combination of several types including: Instance, Electronic.
The Resource
R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully, Raghave Bali, Dipanjan Sarkar
Resource Information
The instance R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully, Raghave Bali, Dipanjan Sarkar represents a material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries. This resource is a combination of several types including: Instance, Electronic.
 Label
 R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully, Raghave Bali, Dipanjan Sarkar
 Title remainder
 understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully
 Statement of responsibility
 Raghave Bali, Dipanjan Sarkar
 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
 Builtin functionsUserdefined functions; Passing functions as arguments; Controlling code flow; Working with if, ifelse, 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; KNearest Neighbors (KNN); Unsupervised learning algorithms; Apriori algorithm; KMeans; 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
 Record ID
 99149474932701651
 Specific material designation
 remote
 System control number

 (CKB)3710000000635652
 (EBL)4520645
 (MiAaPQ)EBC4520645
 (EXLCZ)993710000000635652
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