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Data Science and Predictive Analytics

Overview of attention for book
Cover of 'Data Science and Predictive Analytics'

Table of Contents

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    Book Overview
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    Chapter 1 Motivation
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    Chapter 2 Foundations of R
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    Chapter 3 Managing Data in R
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    Chapter 4 Data Visualization
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    Chapter 5 Linear Algebra & Matrix Computing
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    Chapter 6 Dimensionality Reduction
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    Chapter 7 Lazy Learning: Classification Using Nearest Neighbors
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    Chapter 8 Probabilistic Learning: Classification Using Naive Bayes
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    Chapter 9 Decision Tree Divide and Conquer Classification
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    Chapter 10 Forecasting Numeric Data Using Regression Models
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    Chapter 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
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    Chapter 12 Apriori Association Rules Learning
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    Chapter 13 k-Means Clustering
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    Chapter 14 Model Performance Assessment
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    Chapter 15 Improving Model Performance
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    Chapter 16 Specialized Machine Learning Topics
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    Chapter 17 Variable/Feature Selection
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    Chapter 18 Regularized Linear Modeling and Controlled Variable Selection
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    Chapter 19 Big Longitudinal Data Analysis
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    Chapter 20 Natural Language Processing/Text Mining
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    Chapter 21 Prediction and Internal Statistical Cross Validation
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    Chapter 22 Function Optimization
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    Chapter 23 Deep Learning, Neural Networks
Attention for Chapter 5: Linear Algebra & Matrix Computing
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