دانلود کتاب Data Science: Concepts and Practice

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Cover Data Science: Concepts and Practice Copyright Dedication Foreword Preface Why Data Science? Why This Book? Who Can Use This Book? Acknowledgments 1 Introduction 1.1 AI, Machine learning, and Data Science 1.2 What is Data Science? 1.2.1 Extracting Meaningful Patterns 1.2.2 Building Representative Models 1.2.3 Combination of Statistics, Machine Learning, and Computing 1.2.4 Learning Algorithms 1.2.5 Associated Fields 1.3 Case for Data Science 1.3.1 Volume 1.3.2 Dimensions 1.3.3 Complex Questions 1.4 Data Science Classification 1.5 Data Science Algorithms 1.6 Roadmap for This Book 1.6.1 Getting Started With Data Science 1.6.2 Practice using RapidMiner 1.6.3 Core Algorithms References 2 Data Science Process 2.1 Prior Knowledge 2.1.1 Objective 2.1.2 Subject Area 2.1.3 Data 2.1.4 Causation Versus Correlation 2.2 Data Preparation 2.2.1 Data Exploration 2.2.2 Data Quality 2.2.3 Missing Values 2.2.4 Data Types and Conversion 2.2.5 Transformation 2.2.6 Outliers 2.2.7 Feature Selection 2.2.8 Data Sampling 2.3 Modeling 2.3.1 Training and Testing Datasets 2.3.2 Learning Algorithms 2.3.3 Evaluation of the Model 2.3.4 Ensemble Modeling 2.4 Application 2.4.1 Production Readiness 2.4.2 Technical Integration 2.4.3 Response Time 2.4.4 Model Refresh 2.4.5 Assimilation 2.5 Knowledge References 3 Data Exploration 3.1 Objectives of Data Exploration 3.2 Datasets 3.2.1 Types of Data Numeric or Continuous Categorical or Nominal 3.3 Descriptive Statistics 3.3.1 Univariate Exploration Measure of Central Tendency Measure of Spread 3.3.2 Multivariate Exploration Central Data Point Correlation 3.4 Data Visualization 3.4.1 Univariate Visualization Histogram Quartile Distribution Chart 3.4.2 Multivariate Visualization Scatterplot Scatter Multiple Scatter Matrix Bubble Chart Density Chart 3.4.3 Visualizing High-Dimensional Data Parallel Chart Deviation Chart Andrews Curves 3.5 Roadmap for Data Exploration References 4 Classification 4.1 Decision Trees 4.1.1 How It Works Step 1: Where to Split Data? Step 2: When to Stop Splitting Data? 4.1.2 How to Implement Implementation 1: To Play Golf or Not? Implementation 2: Prospect Filtering Step 1: Data Preparation Step 2: Divide dataset Into Training and Testing Samples Step 3: Modeling Operator and Parameters Step 4: Configuring the Decision Tree Model Step 5: Process Execution and Interpretation 4.1.3 Conclusion 4.2 Rule Induction WARNING!!! DUMMY ENTRY Approaches to Developing a Rule Set 4.2.1 How It Works Step 1: Class Selection Step 2: Rule Development Step 3: Learn-One-Rule Step 4: Next Rule Step 5: Development of Rule Set 4.2.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Results Interpretation Alternative Approach: Tree-to-Rules 4.2.3 Conclusion 4.3 k-Nearest Neighbors 4.3.1 How It Works Measure of Proximity Distance Weights Correlation similarity Simple matching coefficient Jaccard similarity Cosine similarity 4.3.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Execution and Interpretation 4.3.3 Conclusion 4.4 Naïve Bayesian 4.4.1 How It Works Step 1: Calculating Prior Probability P(Y) Step 2: Calculating Class Conditional Probability P(Xi|Y) Step 3: Predicting the Outcome Using Bayes’ Theorem Issue 1: Incomplete Training Set Issue 2: Continuous Attributes Issue 3: Attribute Independence 4.4.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Evaluation Step 4: Execution and Interpretation 4.4.3 Conclusion 4.5 Artificial Neural Networks 4.5.1 How It Works Step 1: Determine the Topology and Activation Function Step 2: Initiation Step 3: Calculating Error Step 4: Weight Adjustment 4.5.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Evaluation Step 4: Execution and Interpretation 4.5.3 Conclusion 4.6 Support Vector Machines WARNING!!! DUMMY ENTRY Concept and Terminology 4.6.1 How It Works 4.6.2 How to Implement Implementation 1: Linearly Separable Dataset Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Process Execution and Interpretation Example 2: Linearly Non-Separable Dataset Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Execution and Interpretation Parameter Settings 4.6.3 Conclusion 4.7 Ensemble Learners WARNING!!! DUMMY ENTRY Wisdom of the Crowd 4.7.1 How It Works Achieving the Conditions for Ensemble Modeling 4.7.2 How to Implement Ensemble by Voting Bootstrap Aggregating or Bagging Implementation Boosting AdaBoost Implementation Random Forest Implementation 4.7.3 Conclusion References 5 Regression Methods 5.1 Linear Regression 5.1.1 How it Works 5.1.2 How to Implement Step 1: Data Preparation Step 2: Model Building Step 3: Execution and Interpretation Step 4: Application to Unseen Test Data 5.1.3 Checkpoints 5.2 Logistic Regression 5.2.1 How It Works How Does Logistic Regression Find the Sigmoid Curve? A Simple but Tragic Example 5.2.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Execution and Interpretation Step 4: Using MetaCost Step 5: Applying the Model to an Unseen Dataset 5.2.3 Summary Points 5.3 Conclusion References 6 Association Analysis 6.1 Mining Association Rules 6.1.1 Itemsets Support Confidence Lift Conviction 6.1.2 Rule Generation 6.2 Apriori Algorithm 6.2.1 How it Works Frequent Itemset Generation Rule Generation 6.3 Frequent Pattern-Growth Algorithm 6.3.1 How it Works Frequent Itemset Generation 6.3.2 How to Implement Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Create Association Rules Step 4: Interpreting the Results 6.4 Conclusion References 7 Clustering Clustering to Describe the Data Clustering for Preprocessing Types of Clustering Techniques 7.1 k-Means Clustering 7.1.1 How It Works Step 1: Initiate Centroids Step 2: Assign Data Points Step 3: Calculate New Centroids Step 4: Repeat Assignment and Calculate New Centroids Step 5: Termination Special Cases Evaluation of Clusters 7.1.2 How to Implement Step 1: Data Preparation Step 2: Clustering Operator and Parameters Step 3: Evaluation Step 4: Execution and Interpretation 7.2 DBSCAN Clustering 7.2.1 How It Works Step 1: Defining Epsilon and MinPoints Step 2: Classification of Data Points Step 3: Clustering Optimizing Parameters Special Cases: Varying Densities 7.2.2 How to Implement Step 1: Data Preparation Step 2: Clustering Operator and Parameters Step 3: Evaluation Step 4: Execution and Interpretation 7.3 Self-Organizing Maps 7.3.1 How It Works Step 1: Topology Specification Step 2: Initialize Centroids Step 3: Assignment of Data Objects Step 4: Centroid Update Step 5: Termination Step 6: Mapping a New Data Object 7.3.2 How to Implement Step 1: Data Preparation Step 2: SOM Modeling Operator and Parameters Step 3: Execution and Interpretation Visual Model Location Coordinates Conclusion References 8 Model Evaluation 8.1 Confusion Matrix 8.2 ROC and AUC 8.3 Lift Curves 8.4 How to Implement WARNING!!! DUMMY ENTRY Step 1: Data Preparation Step 2: Modeling Operator and Parameters Step 3: Evaluation Step 4: Execution and Interpretation 8.5 Conclusion References 9 Text Mining 9.1 How It Works 9.1.1 Term Frequency–Inverse Document Frequency 9.1.2 Terminology 9.2 How to Implement 9.2.1 Implementation 1: Keyword Clustering Step 1: Gather Unstructured Data Step 2: Data Preparation Step 3: Apply Clustering 9.2.2 Implementation 2: Predicting the Gender of Blog Authors Step 1: Gather Unstructured Data Step 2: Data Preparation Step 3.1: Identify Key Features Step 3.2: Build Models Step 4.1: Prepare Test Data for Model Application Step 4.2: Applying the Trained Models to Testing Data Bias in Machine Learning 9.3 Conclusion References 10 Deep Learning 10.1 The AI Winter AI Winter: 1970’s Mid-Winter Thaw of the 1980s The Spring and Summer of Artificial Intelligence: 2006—Today 10.2 How it works 10.2.1 Regression Models As Neural Networks 10.2.2 Gradient Descent 10.2.3 Need for Backpropagation 10.2.4 Classifying More Than 2 Classes: Softmax 10.2.5 Convolutional Neural Networks 10.2.6 Dense Layer 10.2.7 Dropout Layer 10.2.8 Recurrent Neural Networks 10.2.9 Autoencoders 10.2.10 Related AI Models 10.3 How to Implement WARNING!!! DUMMY ENTRY Handwritten Image Recognition Step 1: Dataset Preparation Step 2: Modeling using the Keras Model Step 3: Applying the Keras Model Step 4: Results 10.4 Conclusion References 11 Recommendation Engines Why Do We Need Recommendation Engines? Applications of Recommendation Engines 11.1 Recommendation Engine Concepts WARNING!!! DUMMY ENTRY Building up the Ratings Matrix Step 1: Assemble Known Ratings Step 2: Rating Prediction Step 3: Evaluation The Balance 11.1.1 Types of Recommendation Engines 11.2 Collaborative Filtering 11.2.1 Neighborhood-Based Methods User-Based Collaborative Filtering Step 1: Identifying Similar Users Step 2: Deducing Rating From Neighborhood Users Item-Based Collaborative Filtering User-Based or Item-Based Collaborative Filtering? Neighborhood based Collaborative Filtering - How to Implement Dataset Implementation Steps Conclusion 11.2.2 Matrix Factorization Matrix Factorization - How to Implement Implementation Steps 11.3 Content-Based Filtering WARNING!!! DUMMY ENTRY Building an Item Profile 11.3.1 User Profile Computation Content-Based Filtering - How to Implement Dataset Implementation steps 11.3.2 Supervised Learning Models Supervised Learning Models - How to Implement Dataset Implementation steps 11.4 Hybrid Recommenders 11.5 Conclusion WARNING!!! DUMMY ENTRY Summary of the Types of Recommendation Engines References 12 Time Series Forecasting Taxonomy of Time Series Forecasting 12.1 Time Series Decomposition 12.1.1 Classical Decomposition 12.1.2 How to Implement Forecasting Using Decomposed Data 12.2 Smoothing Based Methods 12.2.1 Simple Forecasting Methods Naïve Method Seasonal Naive Method Average Method Moving Average Smoothing Weighted Moving Average Smoothing 12.2.2 Exponential Smoothing Holt’s Two-Parameter Exponential Smoothing Holt-Winters’ Three-Parameter Exponential Smoothing 12.2.3 How to Implement R Script for Holt-Winters’ Forecasting 12.3 Regression Based Methods 12.3.1 Regression 12.3.2 Regression With Seasonality How to implement 12.3.3 Autoregressive Integrated Moving Average Autocorrelation Autoregressive Models Stationary Data Differencing Moving Average of Error Autoregressive Integrated Moving Average How to Implement 12.3.4 Seasonal ARIMA How to Implement 12.4 Machine Learning Methods 12.4.1 Windowing Model Training How to Implement Step 1: Set Up Windowing Step 2: Train the Model Step 3: Generate the Forecast in a Loop 12.4.2 Neural Network Autoregressive How to Implement 12.5 Performance Evaluation 12.5.1 Validation Dataset Mean Absolute Error Root Mean Squared Error Mean Absolute Percentage Error Mean Absolute Scaled Error 12.5.2 Sliding Window Validation 12.6 Conclusion 12.6.1 Forecasting Best Practices References 13 Anomaly Detection 13.1 Concepts 13.1.1 Causes of Outliers 13.1.2 Anomaly Detection Techniques Outlier Detection Using Statistical Methods Outlier Detection Using Data Science 13.2 Distance-Based Outlier Detection 13.2.1 How It Works 13.2.2 How to Implement Step 1: Data Preparation Step 2: Detect Outlier Operator Step 3: Execution and Interpretation 13.3 Density-Based Outlier Detection 13.3.1 How It Works 13.3.2 How to Implement Step 1: Data Preparation Step 2: Detect Outlier Operator Step 3: Execution and Interpretation 13.4 Local Outlier Factor 13.4.1 How it Works 13.4.2 How to Implement Step 1: Data Preparation Step 2: Detect Outlier Operator Step 3: Results Interpretation 13.5 Conclusion References 14 Feature Selection 14.1 Classifying Feature Selection Methods 14.2 Principal Component Analysis 14.2.1 How It Works 14.2.2 How to Implement Step 1: Data Preparation Step 2: PCA Operator Step 3: Execution and Interpretation 14.3 Information Theory-Based Filtering 14.4 Chi-Square-Based Filtering 14.5 Wrapper-Type Feature Selection 14.5.1 Backward Elimination 14.6 Conclusion References 15 Getting Started with RapidMiner 15.1 User Interface and Terminology WARNING!!! DUMMY ENTRY Terminology 15.2 Data Importing and Exporting Tools 15.3 Data Visualization Tools WARNING!!! DUMMY ENTRY Univariate Plots Bivariate Plots Multivariate Plots 15.4 Data Transformation Tools 15.5 Sampling and Missing Value Tools 15.6 Optimization Tools5 15.7 Integration with R 15.8 Conclusion References Comparison of Data Science Algorithms About the Authors Vijay Kotu Bala Deshpande, PhD Index Praise Back Cover