R Machine Learning By Example

 
 
Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 31. März 2016
  • |
  • 340 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-78439-263-5 (ISBN)
 
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfullyAbout This BookGet to grips with the concepts of machine learning through exciting real-world examplesVisualize and solve complex problems by using power-packed R constructs and its robust packages for machine learningLearn to build your own machine learning system with this example-based practical guideWho This Book Is ForIf 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 LearnUtilize the power of R to handle data extraction, manipulation, and exploration techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresExplore the underlying mathematical and logical concepts that drive machine learning algorithmsDive deep into the world of analytics to predict situations correctlyImplement R machine learning algorithms from scratch and be amazed to see the algorithms in actionWrite reusable code and build complete machine learning systems from the ground upSolve interesting real-world problems using machine learning and R as the journey unfoldsHarness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data scienceIn DetailData 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 machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.Style and approachThe book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
  • 3,17 MB
978-1-78439-263-5 (9781784392635)
1784392634 (1784392634)
weitere Ausgaben werden ermittelt
Raghav Bali has a master's degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies in the world. Raghav is a shutterbug, capturing moments when he isn't busy solving problems. Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He received his master's degree in information technology from the International Institute of Information Technology, Bangalore. His areas of specialization includes software engineering, data science, machine learning, and text analytics.
Dipanjan's interests include learning about new technology, disruptive start-ups, and data science. In his spare time, he loves reading, playing games, and watching popular sitcoms. He has also reviewed Data Analysis with R, Learning R for Geospatial Analysis, and R Data Analysis Cookbook, all by Packt Publishing.
  • 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 functions
  • User-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 Learn
  • Understanding 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 data
  • Analyzing 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 filters
  • Core 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
  • Building analysis utilities
  • Analyzing the dataset
  • Saving the transformed dataset
  • Next steps
  • Feature sets
  • Machine learning algorithms
  • Summary
  • Chapter 6: Credit Risk Detection and Prediction - Predictive Analytics
  • Predictive analytics
  • How to predict credit risk
  • Important concepts in predictive modeling
  • Preparing the data
  • Building predictive models
  • Evaluating predictive models
  • Getting the data
  • Data preprocessing
  • Feature selection
  • Modeling using logistic regression
  • Modeling using support vector machines
  • Modeling using decision trees
  • Modeling using random forests
  • Modeling using neural networks
  • Model comparison and selection
  • Summary
  • Chapter 7: Social Media Analysis - Analyzing Twitter Data
  • Social networks (Twitter)
  • Data mining @social networks
  • Mining social network data
  • Data and visualization
  • Word clouds
  • Treemaps
  • Pixel-oriented maps
  • Other visualizations
  • Getting started with Twitter APIs
  • Overview
  • Registering the application
  • Connect/authenticate
  • Extracting sample tweets
  • Twitter data mining
  • Frequent words and associations
  • Popular devices
  • Hierarchical clustering
  • Topic modeling
  • Challenges with social network data mining
  • References
  • Summary
  • Chapter 8: Sentiment Analysis of Twitter Data
  • Understanding Sentiment Analysis
  • Key concepts of sentiment analysis
  • Subjectivity
  • Sentiment polarity
  • Opinion summarization
  • Feature extraction
  • Approaches
  • Applications
  • Challenges
  • Sentiment analysis upon Tweets
  • Polarity analysis
  • Classification-based algorithms
  • Labeled dataset
  • Support Vector Machines
  • Ensemble methods
  • Summary
  • Index

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