RÓïÑÔ»úÆ÷ѧϰ-µÚ2°æ-(Ó°Ó¡°æ)

Preface Chapter 1: Introducing Machine LearningThe origins of machine learningUses and abuses of machine learningMachine learning successesThe limits of machine learningMachine learning ethicsHow machines learnData storageAbstractionGeneralizationEvaluationMachine learning in practiceTypes of input dataTypes of machine learning algorithmsMatching input data to algorithmsMachine learning with RInstalling R packagesLoading and unloading R packagesSummary Chapter 2: Managing and Understanding Data R data structuresVectorsFactorsListsData framesMatrixes and arraysManaging data with RSaving, loading, and removing R data structuresImporting and saving data from CSV filesExploring and understanding dataExploring the structure of dataExploring numeric variablesMeasuring the central tendency- mean and medianMeasuring spread - quartiles and the five-number summaryVisualizing numeric variables - boxplotsVisualizing numeric variables - histogramsUnderstanding numeric data - uniform and normal distributionsMeasuring spread - variance and standard deviationExploring categorical variablesMeasuring the central tendency - the modeExploring relationships between variablesVisualizing relationships - scatterplotsExamining relationships - two-way cross-tabulationsSummary Chapter 3: Lazy Learning - Classification Using Nearest NeighborsUnderstanding nearest neighbor classificationThe k-NN algorithmMeasuring similarity with distanceChoosing an appropriate kPreparing data for use with k-NNWhy is the k-NN algorithm lazy?Example - diagnosing breast cancer with the k-NN algorithmStep 1 - collecting dataStep 2 - exploring and preparing the dataTransformation - normalizing numeric dataData preparation - creating training and test datasetsStep 3 - training a model on the dataStep 4 - evaluating model performanceStep 5 -improving model performanceTransformation - z-score standardizationTesting alternative values of kSummary Chapter 4: Probabilistic Learning - Classification Using Naive BayesUnderstanding Naive BayesBasic concepts of Bayesian methodsUnderstanding probabilityUnderstanding joint probabilityComputing conditional probability with Bayes' theoremThe Naive Bayes algorithmClassification with Naive BayesThe Laplace estimatorUsing numeric features with Naive BayesExample - filtering mobile phone spam with theNaive Bayes algorithmStep 1 - collecting dataStep 2 - exploring and preparing the dataData preparation - cleaning and standardizing text dataData preparation - splitting text documents into wordsData preparation - creating training and test datasetsVisualizing text data - word cloudsData preparation - creating indicator features for frequent wordsStep 3 - training a model on the dataStep 4 - evaluating model performanceStep 5 -improving model performanceSummary Chapter 5: Divide and Conquer - Classification Using Decision Trees and Rules Chapter 6: Forecasting Numeric Data - Regression Methods Chapter 7: Black Box Methods - Neural Networks and Support Vector Machines Chapter 8: Finding Patterns - Market Basket Analysis Using Association Rules Chapter 9: Finding Groups of Data - Clustering with k-means Chapter 10: Evaluating Model Performance Chapter 11: Improving Model Performance Chapter 12: Specialized Machine Learning Topics Index