Machine Learning Using R - With Time Series and Industry-Based Use Cases in R (2nd Edition)
Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programmin...
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| Main Authors | , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Berkeley, CA
Apress, an imprint of Springer Nature
2019
Apress L. P Apress |
| Edition | 2 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1484242149 9781484242148 9781484242155 1484242157 |
| DOI | 10.1007/978-1-4842-4215-5 |
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| Abstract | Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download.This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots. |
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| AbstractList | Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download.This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots. Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.What You'll Learn Understand machine learning algorithms using RMaster the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithmsSee industry focused real-world use casesTackle time series modeling in RApply deep learning using Keras and TensorFlow in RWho This Book is ForData scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R. Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.What You'll Learn Understand machine learning algorithms using RMaster the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithmsSee industry focused real-world use casesTackle time series modeling in RApply deep learning using Keras and TensorFlow in RWho This Book is ForData scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R. |
| Author | Singh, Abhishek Ramasubramanian, Karthik |
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| Snippet | Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a... Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work... |
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| SubjectTerms | Artificial Intelligence Computer Science Machine learning Open Source Professional and Applied Computing Programming Languages Programming Languages, Compilers, Interpreters R (Computer program language) Software Engineering |
| TableOfContents | Title Page
Introduction
Table of Contents
1. Introduction to Machine Learning and R
2. Data Preparation and Exploration
3. Sampling and Resampling Techniques
4. Data Visualization in R
5. Feature Engineering
6. Machine Learning Theory and Practice
7. Machine Learning Model Evaluation
8. Model Performance Improvement
9. Time Series Modeling
10. Scalable Machine Learning and Related Technologies
11. Deep Learning Using Keras and TensorFlow
Index 3.10.1.3 Steps in Simulation with R Code -- 3.10.2 Central Limit Theorem -- 3.10.2.1 Steps in Simulation with R Code -- 3.11 Probability Sampling Techniques -- 3.11.1 Population Statistics -- 3.11.2 Simple Random Sampling -- 3.11.3 Systematic Random Sampling -- 3.11.4 Stratified Random Sampling -- 3.11.5 Cluster Sampling -- 3.11.6 Bootstrap Sampling -- 3.12 Monte Carlo Method: Acceptance-Rejection Method -- 3.13 Summary -- Chapter 4: Data Visualization in R -- 4.1 Introduction to the ggplot2 Package -- 4.2 World Development Indicators -- 4.3 Line Chart -- 4.4 Stacked Column Charts -- 4.5 Scatterplots -- 4.6 Boxplots -- 4.7 Histograms and Density Plots -- 4.8 Pie Charts -- 4.9 Correlation Plots -- 4.10 Heatmaps -- 4.11 Bubble Charts -- 4.12 Waterfall Charts -- 4.13 Dendogram -- 4.14 Wordclouds -- 4.15 Sankey Plots -- 4.16 Time Series Graphs -- 4.17 Cohort Diagrams -- 4.18 Spatial Maps -- 4.19 Summary -- Chapter 5: Feature Engineering -- 5.1 Introduction to Feature Engineering -- 5.2 Understanding the Data -- 5.2.1 Data Summary -- 5.2.2 Properties of Dependent Variable -- 5.2.3 Features Availability: Continuous or Categorical -- 5.2.4 Setting Up Data Assumptions -- 5.3 Feature Ranking -- 5.4 Variable Subset Selection -- 5.4.1 Filter Method -- 5.4.2 Wrapper Methods -- 5.4.3 Embedded Methods -- 5.5 Principal Component Analysis -- 5.6 Summary -- Chapter 6: Machine Learning Theory and Practice -- 6.1 Machine Learning Types -- 6.1.1 Supervised Learning -- 6.1.2 Unsupervised Learning -- 6.1.3 Semi-Supervised Learning -- 6.1.4 Reinforcement Learning -- 6.2 Groups of Machine Learning Algorithms -- 6.3 Real-World Datasets -- 6.3.1 House Sale Prices -- 6.3.2 Purchase Preference -- 6.3.3 Twitter Feeds and Article -- 6.3.4 Breast Cancer -- 6.3.5 Market Basket -- 6.3.6 Amazon Food Reviews -- 6.4 Regression Analysis -- 6.5 Correlation Analysis Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Machine Learning and R -- 1.1 Understanding the Evolution -- 1.1.1 Statistical Learning -- 1.1.2 Machine Learning (ML) -- 1.1.3 Artificial Intelligence (AI) -- 1.1.4 Data Mining -- 1.1.5 Data Science -- 1.2 Probability and Statistics -- 1.2.1 Counting and Probability Definition -- 1.2.2 Events and Relationships -- 1.2.2.1 Independent Events -- 1.2.2.2 Conditional Independence -- 1.2.2.3 Bayes Theorem -- 1.2.3 Randomness, Probability, and Distributions -- 1.2.4 Confidence Interval and Hypothesis Testing -- 1.2.4.1 Confidence Interval -- 1.2.4.2 Hypothesis Testing -- 1.3 Getting Started with R -- 1.3.1 Basic Building Blocks -- 1.3.1.1 Calculations -- 1.3.1.2 Statistics with R -- 1.3.1.3 Packages -- 1.3.2 Data Structures in R -- 1.3.2.1 Vectors -- 1.3.2.2 Lists -- 1.3.2.3 Matrixes -- 1.3.2.4 Data Frames -- 1.3.3 Subsetting -- 1.3.3.1 Vectors -- 1.3.3.2 Lists -- 1.3.3.3 Matrixes -- 1.3.3.4 Data Frames -- 1.3.4 Functions and the Apply Family -- 1.4 Machine Learning Process Flow -- 1.4.1 Plan -- 1.4.2 Explore -- 1.4.3 Build -- 1.4.4 Evaluate -- 1.5 Other Technologies -- 1.6 Summary -- Chapter 2: Data Preparation and Exploration -- 2.1 Planning the Gathering of Data -- 2.1.1 Variables Types -- 2.1.1.1 Categorical Variables -- 2.1.1.2 Continuous Variables -- 2.1.2 Data Formats -- 2.1.2.1 Comma-Separated Values -- 2.1.2.2 XLS Files -- 2.1.2.3 Extensible Markup Language: XML -- 2.1.2.4 Hypertext Markup Language: HTML -- 2.1.2.5 JSON -- 2.1.3 Types of Data Sources -- 2.1.3.1 Structured Data -- 2.1.3.2 Semi-Structured Data -- 2.1.3.3 Unstructured Data -- 2.2 Initial Data Analysis (IDA) -- 2.2.1 Discerning a First Look -- 2.2.1.1 Function str() -- 2.2.1.2 Naming Convention: make.names() 2.2.1.3 Table(): Pattern or Trend -- 2.2.2 Organizing Multiple Sources of Data into One -- 2.2.2.1 Merge and dplyr Joins -- 2.2.2.1.1 Using merge -- 2.2.2.1.2 dplyr -- 2.2.3 Cleaning the Data -- 2.2.3.1 Correcting Factor Variables -- 2.2.3.2 Dealing with NAs -- 2.2.3.3 Dealing with Dates and Times -- 2.2.3.3.1 Time Zone -- 2.2.3.3.2 Daylight Savings Time -- 2.2.4 Supplementing with More Information -- 2.2.4.1 Derived Variables -- 2.2.4.2 n-Day Averages -- 2.2.5 Reshaping -- 2.3 Exploratory Data Analysis -- 2.3.1 Summary Statistics -- 2.3.1.1 Quantile -- 2.3.1.2 Mean -- 2.3.1.3 Frequency Plot -- 2.3.1.4 Boxplot -- 2.3.2 Moment -- 2.3.2.1 Variance -- 2.3.2.2 Skewness -- 2.3.2.3 Kurtosis -- 2.4 Case Study: Credit Card Fraud -- 2.4.1 Data Import -- 2.4.2 Data Transformation -- 2.4.3 Data Exploration -- 2.5 Summary -- Chapter 3: Sampling and Resampling Techniques -- 3.1 Introduction to Sampling -- 3.2 Sampling Terminology -- 3.2.1 Sample -- 3.2.2 Sampling Distribution -- 3.2.3 Population Mean and Variance -- 3.2.4 Sample Mean and Variance -- 3.2.5 Pooled Mean and Variance -- 3.2.6 Sample Point -- 3.2.7 Sampling Error -- 3.2.8 Sampling Fraction -- 3.2.9 Sampling Bias -- 3.2.10 Sampling Without Replacement (SWOR) -- 3.2.11 Sampling with Replacement (SWR) -- 3.3 Credit Card Fraud: Population Statistics -- 3.4 Data Description -- 3.5 Population Mean -- 3.6 Population Variance -- 3.7 Pooled Mean and Variance -- 3.8 Business Implications of Sampling -- 3.8.1 Shortcomings of Sampling -- 3.9 Probability and Non-Probability Sampling -- 3.9.1 Types of Non-Probability Sampling -- 3.9.1.1 Convenience Sampling -- 3.9.1.2 Purposive Sampling -- 3.9.1.3 Quota Sampling -- 3.10 Statistical Theory on Sampling Distributions -- 3.10.1 Law of Large Numbers: LLN -- 3.10.1.1 Weak Law of Large Numbers -- 3.10.1.2 Strong Law of Large Numbers 6.9.2.2 Centroid-Based Clustering -- 6.9.2.3 Distribution-Based Clustering -- 6.9.2.4 Density-Based Clustering -- 6.9.3 Internal Evaluation -- 6.9.3.1 Dunn Index -- 6.9.3.2 Silhouette Coefficient -- 6.9.4 External Evaluation -- 6.9.4.1 Rand Measure -- 6.9.4.2 Jaccard Index -- 6.9.5 Conclusion -- 6.10 Association Rule Mining -- 6.10.1 Introduction to Association Concepts -- 6.10.1.1 Support -- 6.10.1.2 Confidence -- 6.10.1.3 Lift -- 6.10.2 Rule-Mining Algorithms -- 6.10.2.1 Apriori -- 6.10.2.2 Eclat -- 6.10.3 Recommendation Algorithms -- 6.10.3.1 User-Based Collaborative Filtering (UBCF) -- 6.10.3.2 Item-Based Collaborative Filtering (IBCF) -- 6.10.4 Conclusion -- 6.11 Artificial Neural Networks -- 6.11.1 Human Cognitive Learning -- 6.11.2 Perceptron -- 6.11.3 Sigmoid Neuron -- 6.11.4 Neural Network Architecture -- 6.11.5 Supervised versus Unsupervised Neural Nets -- 6.11.6 Neural Network Learning Algorithms -- 6.11.6.1 Evolutionary Methods -- 6.11.6.2 Gene Expression Programming -- 6.11.6.3 Simulated Annealing -- 6.11.6.4 Expectation Maximization -- 6.11.6.5 Non-Parametric Methods -- 6.11.6.6 Particle Swarm Optimization -- 6.11.7 Feed-Forward Back-Propagation -- 6.11.7.1 Purchase Prediction: Neural Network-Based Classification -- 6.11.8 Conclusion -- 6.12 Text-Mining Approaches -- 6.12.1 Introduction to Text Mining -- 6.12.2 Text Summarization -- 6.12.3 TF-IDF -- 6.12.4 Part-of-Speech (POS) Tagging -- 6.12.5 Word Cloud -- 6.12.6 Text Analysis: Microsoft Cognitive Services -- 6.12.7 Conclusion -- 6.13 Online Machine Learning Algorithms -- 6.13.1 Fuzzy C-Means Clustering -- 6.13.2 Conclusion -- 6.14 Model Building Checklist -- 6.15 Summary -- Chapter 7: Machine Learning Model Evaluation -- 7.1 Dataset -- 7.1.1 House Sale Prices -- 7.1.2 Purchase Preference -- 7.2 Introduction to Model Performance and Evaluation 6.5.1 Linear Regression -- 6.5.2 Simple Linear Regression -- 6.5.3 Multiple Linear Regression -- 6.5.4 Model Diagnostics: Linear Regression -- 6.5.4.1 Influential Point Analysis -- 6.5.4.2 Normality of Residuals -- 6.5.4.3 Multicollinearity -- 6.5.4.4 Residual Auto-Correlation -- 6.5.4.5 Homoscedasticity -- 6.5.5 Polynomial Regression -- 6.5.6 Logistic Regression -- 6.5.7 Logit Transformation -- 6.5.8 Odds Ratio -- 6.5.8.1 Binomial Logistic Model -- 6.5.9 Model Diagnostics: Logistic Regression -- 6.5.9.1 Wald Test -- 6.5.9.2 Deviance -- 6.5.9.3 Pseudo R-Square -- 6.5.9.4 Bivariate Plots -- 6.5.9.5 Cumulative Gains and Lift Charts -- 6.5.9.6 Concordance and Discordant Ratios -- 6.5.10 Multinomial Logistic Regression -- 6.5.11 Generalized Linear Models -- 6.5.12 Conclusion -- 6.6 Support Vector Machine SVM -- 6.6.1 Linear SVM -- 6.6.1.1 Hard Margins -- 6.6.1.2 Soft Margins -- 6.6.2 Binary SVM Classifier -- 6.6.3 Multi-Class SVM -- 6.6.4 Conclusion -- 6.7 Decision Trees -- 6.7.1 Types of Decision Trees -- 6.7.1.1 Regression Trees -- 6.7.1.2 Classification Trees -- 6.7.2 Decision Measures -- 6.7.2.1 Gini Index -- 6.7.2.2 Entropy -- 6.7.2.3 Information Gain -- 6.7.3 Decision Tree Learning Methods -- 6.7.3.1 Iterative Dichotomizer 3 -- 6.7.3.2 C5.0 Algorithm -- 6.7.3.3 Classification and Regression Tree: CART -- 6.7.3.4 Chi-Square Automated Interaction Detection: CHAID -- 6.7.4 Ensemble Trees -- 6.7.4.1 Boosting -- 6.7.4.2 Bagging -- Bagging CART -- Random Forest -- 6.7.5 Conclusion -- 6.8 The Naive Bayes Method -- 6.8.1 Conditional Probability -- 6.8.2 Bayes Theorem -- 6.8.3 Prior Probability -- 6.8.4 Posterior Probability -- 6.8.5 Likelihood and Marginal Likelihood -- 6.8.6 Naïve Bayes Methods -- 6.8.7 Conclusion -- 6.9 Cluster Analysis -- 6.9.1 Introduction to Clustering -- 6.9.2 Clustering Algorithms -- 6.9.2.1 Hierarchal Clustering 7.3 Objectives of Model Performance Evaluation |
| Title | Machine Learning Using R - With Time Series and Industry-Based Use Cases in R (2nd Edition) |
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