{"product_id":"practical-machine-learning-in-r-isbn-9781119591511","title":"Practical Machine Learning in R","description":"\u003cp\u003e\u003cb\u003eGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. \u003ci\u003ePractical Machine Learning in R\u003c\/i\u003e provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. \u003c\/p\u003e \u003cp\u003eBestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. \u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores data management techniques, including data collection, exploration and dimensionality reduction\u003c\/li\u003e \u003cli\u003eCovers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering\u003c\/li\u003e \u003cli\u003eDescribes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques\u003c\/li\u003e \u003cli\u003eExplains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePractical Machine Learning in R \u003c\/i\u003eis a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.\u003c\/p\u003e \u003cp\u003eAbout the Authors vii\u003c\/p\u003e \u003cp\u003eAbout the Technical Editors ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003eIntroduction xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Getting Started 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 What is Machine Learning? 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscovering Knowledge in Data 5\u003c\/p\u003e \u003cp\u003eIntroducing Algorithms 5\u003c\/p\u003e \u003cp\u003eArtificial Intelligence, Machine Learning, and Deep Learning 6\u003c\/p\u003e \u003cp\u003eMachine Learning Techniques 7\u003c\/p\u003e \u003cp\u003eSupervised Learning 8\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 12\u003c\/p\u003e \u003cp\u003eModel Selection 14\u003c\/p\u003e \u003cp\u003eClassification Techniques 14\u003c\/p\u003e \u003cp\u003eRegression Techniques 15\u003c\/p\u003e \u003cp\u003eSimilarity Learning Techniques 16\u003c\/p\u003e \u003cp\u003eModel Evaluation 16\u003c\/p\u003e \u003cp\u003eClassification Errors 17\u003c\/p\u003e \u003cp\u003eRegression Errors 19\u003c\/p\u003e \u003cp\u003eTypes of Error 20\u003c\/p\u003e \u003cp\u003ePartitioning Datasets 22\u003c\/p\u003e \u003cp\u003eHoldout Method 23\u003c\/p\u003e \u003cp\u003eCross-Validation Methods 23\u003c\/p\u003e \u003cp\u003eExercises 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Introduction to R and RStudio 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWelcome to R 26\u003c\/p\u003e \u003cp\u003eR and RStudio Components 27\u003c\/p\u003e \u003cp\u003eThe R Language 27\u003c\/p\u003e \u003cp\u003eRStudio 28\u003c\/p\u003e \u003cp\u003eRStudio Desktop 28\u003c\/p\u003e \u003cp\u003eRStudio Server 29\u003c\/p\u003e \u003cp\u003eExploring the RStudio\u003c\/p\u003e \u003cp\u003eEnvironment 29\u003c\/p\u003e \u003cp\u003eR Packages 38\u003c\/p\u003e \u003cp\u003eThe CRAN Repository 38\u003c\/p\u003e \u003cp\u003eInstalling Packages 38\u003c\/p\u003e \u003cp\u003eLoading Packages 39\u003c\/p\u003e \u003cp\u003ePackage Documentation 40\u003c\/p\u003e \u003cp\u003eWriting and Running an R Script 41\u003c\/p\u003e \u003cp\u003eData Types in R 44\u003c\/p\u003e \u003cp\u003eVectors 45\u003c\/p\u003e \u003cp\u003eTesting Data Types 47\u003c\/p\u003e \u003cp\u003eConverting Data Types 50\u003c\/p\u003e \u003cp\u003eMissing Values 51\u003c\/p\u003e \u003cp\u003eExercises 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Managing Data 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Tidyverse 54\u003c\/p\u003e \u003cp\u003eData Collection 55\u003c\/p\u003e \u003cp\u003eKey Considerations 55\u003c\/p\u003e \u003cp\u003eCollecting Ground Truth Data 55\u003c\/p\u003e \u003cp\u003eData Relevance 55\u003c\/p\u003e \u003cp\u003eQuantity of Data 56\u003c\/p\u003e \u003cp\u003eEthics 56\u003c\/p\u003e \u003cp\u003eImporting the Data 56\u003c\/p\u003e \u003cp\u003eReading Comma-Delimited Files 56\u003c\/p\u003e \u003cp\u003eReading Other Delimited Files 60\u003c\/p\u003e \u003cp\u003eData Exploration 60\u003c\/p\u003e \u003cp\u003eDescribing the Data 61\u003c\/p\u003e \u003cp\u003eInstance 61\u003c\/p\u003e \u003cp\u003eFeature 61\u003c\/p\u003e \u003cp\u003eDimensionality 62\u003c\/p\u003e \u003cp\u003eSparsity and Density 62\u003c\/p\u003e \u003cp\u003eResolution 62\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 63\u003c\/p\u003e \u003cp\u003eVisualizing the Data 69\u003c\/p\u003e \u003cp\u003eComparison 69\u003c\/p\u003e \u003cp\u003eRelationship 70\u003c\/p\u003e \u003cp\u003eDistribution 72\u003c\/p\u003e \u003cp\u003eComposition 73\u003c\/p\u003e \u003cp\u003eData Preparation 74\u003c\/p\u003e \u003cp\u003eCleaning the Data 75\u003c\/p\u003e \u003cp\u003eMissing Values 75\u003c\/p\u003e \u003cp\u003eNoise 79\u003c\/p\u003e \u003cp\u003eOutliers 81\u003c\/p\u003e \u003cp\u003eClass Imbalance 82\u003c\/p\u003e \u003cp\u003eTransforming the Data 84\u003c\/p\u003e \u003cp\u003eNormalization 84\u003c\/p\u003e \u003cp\u003eDiscretization 89\u003c\/p\u003e \u003cp\u003eDummy Coding 89\u003c\/p\u003e \u003cp\u003eReducing the Data 92\u003c\/p\u003e \u003cp\u003eSampling 92\u003c\/p\u003e \u003cp\u003eDimensionality Reduction 99\u003c\/p\u003e \u003cp\u003eExercises 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Regression 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Linear Regression 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBicycle Rentals and Regression 104\u003c\/p\u003e \u003cp\u003eRelationships Between Variables 106\u003c\/p\u003e \u003cp\u003eCorrelation 106\u003c\/p\u003e \u003cp\u003eRegression 114\u003c\/p\u003e \u003cp\u003eSimple Linear Regression 115\u003c\/p\u003e \u003cp\u003eOrdinary Least Squares Method 116\u003c\/p\u003e \u003cp\u003eSimple Linear Regression Model 119\u003c\/p\u003e \u003cp\u003eEvaluating the Model 120\u003c\/p\u003e \u003cp\u003eResiduals 121\u003c\/p\u003e \u003cp\u003eCoefficients 121\u003c\/p\u003e \u003cp\u003eDiagnostics 122\u003c\/p\u003e \u003cp\u003eMultiple Linear Regression 124\u003c\/p\u003e \u003cp\u003eThe Multiple Linear Regression Model 124\u003c\/p\u003e \u003cp\u003eEvaluating the Model 125\u003c\/p\u003e \u003cp\u003eResidual Diagnostics 127\u003c\/p\u003e \u003cp\u003eInfluential Point Analysis 130\u003c\/p\u003e \u003cp\u003eMulticollinearity 133\u003c\/p\u003e \u003cp\u003eImproving the Model 135\u003c\/p\u003e \u003cp\u003eConsidering Nonlinear Relationships 135\u003c\/p\u003e \u003cp\u003eConsidering Categorical Variables 137\u003c\/p\u003e \u003cp\u003eConsidering Interactions Between Variables 139\u003c\/p\u003e \u003cp\u003eSelecting the Important Variables 141\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses 146\u003c\/p\u003e \u003cp\u003eCase Study: Predicting Blood Pressure 147\u003c\/p\u003e \u003cp\u003eImporting the Data 148\u003c\/p\u003e \u003cp\u003eExploring the Data 149\u003c\/p\u003e \u003cp\u003eFitting the Simple Linear Regression Model 151\u003c\/p\u003e \u003cp\u003eFitting the Multiple Linear Regression Model 152\u003c\/p\u003e \u003cp\u003eExercises 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Logistic Regression 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProspecting for Potential Donors 166\u003c\/p\u003e \u003cp\u003eClassifi cation 169\u003c\/p\u003e \u003cp\u003eLogistic Regression 170\u003c\/p\u003e \u003cp\u003eOdds Ratio 172\u003c\/p\u003e \u003cp\u003eBinomial Logistic Regression Model 176\u003c\/p\u003e \u003cp\u003eDealing with Missing Data 178\u003c\/p\u003e \u003cp\u003eDealing with Outliers 182\u003c\/p\u003e \u003cp\u003eSplitting the Data 187\u003c\/p\u003e \u003cp\u003eDealing with Class Imbalance 188\u003c\/p\u003e \u003cp\u003eTraining a Model 190\u003c\/p\u003e \u003cp\u003eEvaluating the Model 190\u003c\/p\u003e \u003cp\u003eCoeffi cients 193\u003c\/p\u003e \u003cp\u003eDiagnostics 195\u003c\/p\u003e \u003cp\u003ePredictive Accuracy 195\u003c\/p\u003e \u003cp\u003eImproving the Model 198\u003c\/p\u003e \u003cp\u003eDealing with Multicollinearity 198\u003c\/p\u003e \u003cp\u003eChoosing a Cutoff Value 205\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses 206\u003c\/p\u003e \u003cp\u003eCase Study: Income Prediction 207\u003c\/p\u003e \u003cp\u003eImporting the Data 208\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 208\u003c\/p\u003e \u003cp\u003eTraining the Model 212\u003c\/p\u003e \u003cp\u003eEvaluating the Model 215\u003c\/p\u003e \u003cp\u003eExercises 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Classification 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 \u003ci\u003ek\u003c\/i\u003e-Nearest Neighbors 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDetecting Heart Disease 224\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek\u003c\/i\u003e-Nearest Neighbors 226\u003c\/p\u003e \u003cp\u003eFinding the Nearest Neighbors 228\u003c\/p\u003e \u003cp\u003eLabeling Unlabeled Data 230\u003c\/p\u003e \u003cp\u003eChoosing an Appropriate \u003ci\u003ek \u003c\/i\u003e231\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek\u003c\/i\u003e-Nearest Neighbors Model 232\u003c\/p\u003e \u003cp\u003eDealing with Missing Data 234\u003c\/p\u003e \u003cp\u003eNormalizing the Data 234\u003c\/p\u003e \u003cp\u003eDealing with Categorical Features 235\u003c\/p\u003e \u003cp\u003eSplitting the Data 237\u003c\/p\u003e \u003cp\u003eClassifying Unlabeled Data 237\u003c\/p\u003e \u003cp\u003eEvaluating the Model 238\u003c\/p\u003e \u003cp\u003eImproving the Model 239\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses 241\u003c\/p\u003e \u003cp\u003eCase Study: Revisiting the Donor Dataset 241\u003c\/p\u003e \u003cp\u003eImporting the Data 241\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 242\u003c\/p\u003e \u003cp\u003eDealing with Missing Data 243\u003c\/p\u003e \u003cp\u003eNormalizing the Data 245\u003c\/p\u003e \u003cp\u003eSplitting and Balancing the Data 246\u003c\/p\u003e \u003cp\u003eBuilding the Model 248\u003c\/p\u003e \u003cp\u003eEvaluating the Model 248\u003c\/p\u003e \u003cp\u003eExercises 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Naïve Bayes 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassifying Spam Email 252\u003c\/p\u003e \u003cp\u003eNaïve Bayes 253\u003c\/p\u003e \u003cp\u003eProbability 254\u003c\/p\u003e \u003cp\u003eJoint Probability 255\u003c\/p\u003e \u003cp\u003eConditional Probability 256\u003c\/p\u003e \u003cp\u003eClassification with Naïve Bayes 257\u003c\/p\u003e \u003cp\u003eAdditive Smoothing 261\u003c\/p\u003e \u003cp\u003eNaïve Bayes Model 263\u003c\/p\u003e \u003cp\u003eSplitting the Data 266\u003c\/p\u003e \u003cp\u003eTraining a Model 267\u003c\/p\u003e \u003cp\u003eEvaluating the Model 267\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses of the Naïve Bayes Classifier 269\u003c\/p\u003e \u003cp\u003eCase Study: Revisiting the Heart Disease Detection Problem 269\u003c\/p\u003e \u003cp\u003eImporting the Data 270\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 270\u003c\/p\u003e \u003cp\u003eBuilding the Model 272\u003c\/p\u003e \u003cp\u003eEvaluating the Model 273\u003c\/p\u003e \u003cp\u003eExercises 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Decision Trees 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredicting Build Permit Decisions 278\u003c\/p\u003e \u003cp\u003eDecision Trees 279\u003c\/p\u003e \u003cp\u003eRecursive Partitioning 281\u003c\/p\u003e \u003cp\u003eEntropy 285\u003c\/p\u003e \u003cp\u003eInformation Gain 286\u003c\/p\u003e \u003cp\u003eGini Impurity 290\u003c\/p\u003e \u003cp\u003ePruning 290\u003c\/p\u003e \u003cp\u003eBuilding a Classification Tree Model 291\u003c\/p\u003e \u003cp\u003eSplitting the Data 294\u003c\/p\u003e \u003cp\u003eTraining a Model 295\u003c\/p\u003e \u003cp\u003eEvaluating the Model 295\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses of the Decision Tree Model 298\u003c\/p\u003e \u003cp\u003eCase Study: Revisiting the Income Prediction Problem 299\u003c\/p\u003e \u003cp\u003eImporting the Data 300\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 300\u003c\/p\u003e \u003cp\u003eBuilding the Model 302\u003c\/p\u003e \u003cp\u003eEvaluating the Model 302\u003c\/p\u003e \u003cp\u003eExercises 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Evaluating and Improving\u003c\/b\u003e\u003cb\u003e Performance 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Evaluating Performance 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimating Future Performance 308\u003c\/p\u003e \u003cp\u003eCross-Validation 311\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek\u003c\/i\u003e-Fold Cross-Validation 311\u003c\/p\u003e \u003cp\u003eLeave-One-Out Cross-Validation 315\u003c\/p\u003e \u003cp\u003eRandom Cross-Validation 316\u003c\/p\u003e \u003cp\u003eBootstrap Sampling 318\u003c\/p\u003e \u003cp\u003eBeyond Predictive Accuracy 321\u003c\/p\u003e \u003cp\u003eKappa 323\u003c\/p\u003e \u003cp\u003ePrecision and Recall 326\u003c\/p\u003e \u003cp\u003eSensitivity and Specificity 328\u003c\/p\u003e \u003cp\u003eVisualizing Model Performance 332\u003c\/p\u003e \u003cp\u003eReceiver Operating Characteristic Curve 333\u003c\/p\u003e \u003cp\u003eArea Under the Curve 336\u003c\/p\u003e \u003cp\u003eExercises 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Improving Performance 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eParameter Tuning 342\u003c\/p\u003e \u003cp\u003eAutomated Parameter Tuning 342\u003c\/p\u003e \u003cp\u003eCustomized Parameter Tuning 348\u003c\/p\u003e \u003cp\u003eEnsemble Methods 354\u003c\/p\u003e \u003cp\u003eBagging 355\u003c\/p\u003e \u003cp\u003eBoosting 358\u003c\/p\u003e \u003cp\u003eStacking 361\u003c\/p\u003e \u003cp\u003eExercises 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Unsupervised Learning 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter\u003c\/b\u003e\u003cb\u003e 11 Discovering Patterns with Association Rules 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMarket Basket Analysis 370\u003c\/p\u003e \u003cp\u003eAssociation Rules 371\u003c\/p\u003e \u003cp\u003eIdentifying Strong Rules 373\u003c\/p\u003e \u003cp\u003eSupport 373\u003c\/p\u003e \u003cp\u003eConfi dence 373\u003c\/p\u003e \u003cp\u003eLift 374\u003c\/p\u003e \u003cp\u003eThe Apriori Algorithm 374\u003c\/p\u003e \u003cp\u003eDiscovering Association Rules 376\u003c\/p\u003e \u003cp\u003eGenerating the Rules 377\u003c\/p\u003e \u003cp\u003eEvaluating the Rules 382\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses 386\u003c\/p\u003e \u003cp\u003eCase Study: Identifying Grocery Purchase Patterns 386\u003c\/p\u003e \u003cp\u003eImporting the Data 387\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 387\u003c\/p\u003e \u003cp\u003eGenerating the Rules 389\u003c\/p\u003e \u003cp\u003eEvaluating the Rules 389\u003c\/p\u003e \u003cp\u003eExercises 392\u003c\/p\u003e \u003cp\u003eNotes 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter\u003c\/b\u003e\u003cb\u003e 12 Grouping Data with Clustering 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClustering 396\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek\u003c\/i\u003e-Means Clustering 399\u003c\/p\u003e \u003cp\u003eSegmenting Colleges with \u003ci\u003ek\u003c\/i\u003e-Means Clustering 403\u003c\/p\u003e \u003cp\u003eCreating the Clusters 404\u003c\/p\u003e \u003cp\u003eAnalyzing the Clusters 407\u003c\/p\u003e \u003cp\u003eChoosing the Right Number of Clusters 409\u003c\/p\u003e \u003cp\u003eThe Elbow Method 409\u003c\/p\u003e \u003cp\u003eThe Average Silhouette Method 411\u003c\/p\u003e \u003cp\u003eThe Gap Statistic 412\u003c\/p\u003e \u003cp\u003eStrengths and Weaknesses of \u003ci\u003ek\u003c\/i\u003e-Means Clustering 414\u003c\/p\u003e \u003cp\u003eCase Study: Segmenting Shopping Mall Customers 415\u003c\/p\u003e \u003cp\u003eExploring and Preparing the Data 415\u003c\/p\u003e \u003cp\u003eClustering the Data 416\u003c\/p\u003e \u003cp\u003eEvaluating the Clusters 418\u003c\/p\u003e \u003cp\u003eExercises 420\u003c\/p\u003e \u003cp\u003eNotes 420\u003c\/p\u003e \u003cp\u003eIndex 421\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eFRED NWANGANGA\u003c\/b\u003e, \u003cb\u003eP\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, is an assistant teaching professor of business analytics at the University of Notre Dame's Mendoza College of Business. He has over 15 years of technology leadership experience. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMIKE CHAPPLE\u003c\/b\u003e, \u003cb\u003eP\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, is associate teaching professor of information technology, analytics, and operations at the Mendoza College of Business. Mike is a bestselling author of over 25 books, and he currently serves as academic director of the University's Master of Science in Business Analytics program.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eINTRODUCING MACHINE LEARNING THROUGH THE INTUITIVE R PROGRAMMING LANGUAGE\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMachine learning and data analytics have emerged as important avenues of value creation. Through machine learning, you can discover hidden patterns in data, leading to new ideas and understandings that might remain unknown without this powerful technique. \u003ci\u003ePractical Machine Learning in R\u003c\/i\u003e offers a hands-on introduction to working with large datasets using the R programming language, which is simple to understand and was built specifically for statistical analysis. Even if you have no prior coding experience, this book will show you how data scientists put machine learning into practice to generate business insights, solid predictions, and better decisions. \u003c\/p\u003e\u003cp\u003eUnlike other books on the topic, \u003ci\u003ePractical Machine Learning in R\u003c\/i\u003e provides both a conceptual and technical introduction to machine learning. Examples and exercises use the R programming language and the latest data analytics tools, so you can get started without getting bogged down by advanced mathematics. With this book, machine learning techniques—from logistic regression to association rules and clustering—are within reach. \u003c\/p\u003e\u003cp\u003eThe only book to integrate an intuitive introduction to machine learning with step-by-step technical applications, \u003ci\u003ePractical Machine Learning in R\u003c\/i\u003e shows you how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eConceptualize the different types of machine learning\u003c\/li\u003e \u003cli\u003eDiscover patterns that exist within large datasets\u003c\/li\u003e \u003cli\u003eBegin writing and executing R scripts with RStudio\u003c\/li\u003e \u003cli\u003eUse R with Tidyverse to manage and visualize data\u003c\/li\u003e \u003cli\u003eApply core statistical techniques like logistic regression and Naïve Bayes\u003c\/li\u003e \u003cli\u003eEvaluate and improve upon machine learning models\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989833105637,"sku":"NP9781119591511","price":40.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119591511.jpg?v=1761785617","url":"https:\/\/k12savings.com\/es\/products\/practical-machine-learning-in-r-isbn-9781119591511","provider":"K12savings","version":"1.0","type":"link"}