R - Unleash Machine Learning Techniques (A course in three modules)

Description

Preface......Page 3 Contents......Page 8 --- R Machine Learning by Example......Page 15 Start with R & Machine Learning......Page 16 Delving into the basics of R......Page 17 Data structures in R......Page 22 Working with functions......Page 41 Controlling code flo......Page 44 Advanced constructs......Page 47 Next steps with R......Page 53 Machine learning basics......Page 55 Summary......Page 61 Let's Help Machines Learn......Page 62 Understanding machine learning......Page 63 Algorithms in machine learning......Page 64 Families of algorithms......Page 71 Summary......Page 95 Predicting Customer Shopping Trends with Market Basket Analysis......Page 96 Detecting and predicting trends......Page 97 Market basket analysis......Page 98 Evaluating a product contingency matrix......Page 105 Frequent itemset generation......Page 112 Association rule mining......Page 121 Summary......Page 127 Building a Product Recommendation System......Page 128 Understanding recommendation systems......Page 129 Issues with recommendation systems......Page 130 Collaborative filter......Page 131 Building a recommender engine......Page 137 ∑......Page 139 Production ready recommender engines......Page 148 Summary......Page 157 Analytics......Page 158 Types of analytics......Page 159 Our next challenge......Page 160 What is credit risk?......Page 161 Getting the data......Page 162 Data preprocessing......Page 164 Data analysis and transformation......Page 167 Next steps......Page 196 Summary......Page 198 Analytics......Page 200 Predictive analytics......Page 202 How to predict credit risk......Page 204 Important concepts in predictive modeling......Page 205 Data preprocessing......Page 212 Feature selection......Page 214 Modeling using logistic regression......Page 217 Modeling using support vector machines......Page 222 Modeling using decision trees......Page 233 Modeling using random forests......Page 239 Modeling using neural networks......Page 245 Model comparison and selection......Page 251 Summary......Page 253 Social Media Analysis – Analyzing Twitter Data......Page 254 Social networks (Twitter)......Page 255 Data mining @social networks......Page 257 Getting started with Twitter APIs......Page 263 Twitter data mining......Page 270 Challenges with social network data mining......Page 289 References......Page 290 Summary......Page 291 Sentiment Analysis of Twitter Data......Page 292 Understanding Sentiment Analysis......Page 293 Sentiment analysis upon Tweets......Page 302 Summary......Page 325 --- Machine Learning with R......Page 328 Introducing Machine Learning......Page 330 The origins of machine learning......Page 331 Uses and abuses of machine learning......Page 333 How machines learn......Page 338 Machine learning in practice......Page 345 Machine learning with R......Page 351 Summary......Page 354 Managing and Understanding Data......Page 356 R data structures......Page 357 Managing data with R......Page 368 Exploring and understanding data......Page 371 Summary......Page 393 Lazy Learning – Classificatio Using Nearest Neighbors......Page 394 Understanding nearest neighbor classificatio......Page 395 Example – diagnosing breast cancer with the k-NN algorithm......Page 404 Summary......Page 416 Naive Bayes......Page 418 Understanding Naive Bayes......Page 419 Example – filtering mobile phone spam with the Naive Bayes algorithm......Page 432 Summary......Page 453 Divide and Conquer – Classification Using Decision Trees and Rules......Page 454 Understanding decision trees......Page 455 Example – identifying risky bank loans using C5.0 decision trees......Page 465 Understanding classification rule......Page 478 Example – identifying poisonous mushrooms with rule learners......Page 489 Summary......Page 498 Forecasting Numeric Data – Regression Methods......Page 500 Understanding regression......Page 501 Example – predicting medical expenses using linear regression......Page 515 Understanding regression trees and model trees......Page 530 Example – estimating the quality of wines with regression trees and model trees......Page 534 Summary......Page 547 Black Box Methods – Neural Networks and Support Vector Machines......Page 548 Understanding neural networks......Page 549 Example – Modeling the strength of concrete with ANNs......Page 560 Understanding Support Vector Machines......Page 568 Example – performing OCR with SVMs......Page 577 Summary......Page 586 Finding Patterns – Market Basket Analysis Using Association Rules......Page 588 Understanding association rules......Page 589 Example – identifying frequently purchased groceries with association rules......Page 595 Summary......Page 613 Finding Groups of Data – Clustering with k-means......Page 614 Understanding clustering......Page 615 Example – finding teen market segments using k-means clustering......Page 625 Summary......Page 639 Evaluating Model Performance......Page 640 Measuring performance for classificatio......Page 641 Estimating future performance......Page 665 Summary......Page 673 Improving Model Performance......Page 676 Tuning stock models for better performance......Page 677 Improving model performance with meta-learning......Page 688 Summary......Page 704 Specialized Machine Learning Topics......Page 706 Working with proprietary files and databases......Page 707 Working with online data and services......Page 710 Working with domain-specific dat......Page 721 Improving the performance of R......Page 727 Summary......Page 745 --- Mastering ML with R......Page 746 A Process for Success......Page 748 The process......Page 749 Business understanding......Page 750 Data preparation......Page 753 Modeling......Page 754 Deployment......Page 755 Algorithm flowchar......Page 756 Summary......Page 761 Linear Regression – The Blocking and Tackling of Machine Learning......Page 762 Univariate linear regression......Page 763 Multivariate linear regression......Page 772 Other linear model considerations......Page 787 Summary......Page 791 Logistic Regression and Discriminant Analysis......Page 792 Logistic regression......Page 793 Model selection......Page 816 Summary......Page 821 Advanced Feature Selection in Linear Models......Page 822 Regularization in a nutshell......Page 823 Business case......Page 825 Modeling and evaluation......Page 832 Model selection......Page 850 Summary......Page 851 More Classification Techniques – K-Nearest Neighbors and Support Vector Machines......Page 852 K-Nearest Neighbors......Page 853 Support Vector Machines......Page 854 Business case......Page 858 Feature selection for SVMs......Page 878 Summary......Page 880 Introduction......Page 882 An overview of the techniques......Page 883 Business case......Page 887 Summary......Page 911 Neural Networks......Page 912 Neural network......Page 913 Deep learning, a not-so-deep overview......Page 917 Business understanding......Page 919 Data understanding and preparation......Page 920 Modeling and evaluation......Page 926 An example of deep learning......Page 933 Summary......Page 941 Cluster Analysis......Page 942 Hierarchical clustering......Page 943 K-means clustering......Page 945 Gower and partitioning around medoids......Page 946 Data understanding and preparation......Page 948 Modeling and evaluation......Page 950 Summary......Page 967 Principal Components Analysis......Page 968 An overview of the principal components......Page 969 Modeling and evaluation......Page 980 Summary......Page 991 Market Basket Analysis and Recommendation Engines......Page 992 An overview of a market basket analysis......Page 993 Business understanding......Page 994 Data understanding and preparation......Page 995 Modeling and evaluation......Page 997 An overview of a recommendation engine......Page 1002 Data understanding, preparation, and recommendations......Page 1009 Modeling, evaluation, and recommendations......Page 1012 Summary......Page 1023 Time Series and Causality......Page 1024 Univariate time series analysis......Page 1025 Modeling and evaluation......Page 1040 Summary......Page 1064 Text Mining......Page 1066 Text mining framework and methods......Page 1067 Topic models......Page 1069 Modeling and evaluation......Page 1077 Summary......Page 1091 Getting R up and running......Page 1092 Using R......Page 1101 Data frames and matrices......Page 1105 Summary stats......Page 1107 Installing and loading the R packages......Page 1111 Summary......Page 1112 Biblio......Page 1113