
Premium
Title Page
1/3/2026
Copyright
1/3/2026
Dedication
1/3/2026
About the Author
1/3/2026
Preface
1/3/2026
Table of Contents
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1.1 A Simple Way to Think About It
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1.2 Defining Machine Learning
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1.3 A Brief History of Machine Learning
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Learning
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1.5 The Three Main Types of Machine Learning
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1.6 Machine Learning in Daily Life
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Deep Learning
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1.8 Setting Expectations for the Rest of This Book
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Chapter Summary
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Key Terms in This Chapter
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Questions for Reflection
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2.1 Why a Workflow Matters
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Problem
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2.3 Data Collection and Sources
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2.4 Data Preprocessing Basics
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2.5 Training and Testing Datasets
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2.6 The Model Lifecycle
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2.7 Putting It All Together: A Worked Example
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2.8 Common Pitfalls Across the Workflow
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3.1 Why Preprocessing Matters
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3.2 Data Types: The Foundation of Everything
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3.3 Handling Missing Values
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3.4 Encoding Categorical Variables
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3.5 Feature Scaling
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3.6 Detecting and Handling Outliers
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3.7 Splitting Data into Training, Validation, and Test
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3.8 Building a Reproducible Preprocessing Pipeline
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3.9 Common Pitfalls in Preprocessing
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4.1 What Exploratory Data Analysis Is For
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4.2 Starting with Summary Statistics
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4.3 Looking at Distributions
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4.4 Looking at Relationships Between Variables
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4.5 Quantifying Relationships: Correlation
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4.6 Spotting Outliers Visually
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4.7 A Visualization Toolkit
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4.8 A Worked EDA Example
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4.9 Common Pitfalls in EDA
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5.1 What Is Regression?
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5.2 Linear Regression
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5.3 Cost Functions and Gradient Descent
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5.4 Evaluating Regression Models
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Regression
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5.6 Regularization: Taming Overly Flexible Models
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5.7 Tree-Based Regression
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5.8 A Worked Example: Predicting House Prices
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5.9 Common Pitfalls in Regression
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6.1 What Is Classification?
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6.2 Logistic Regression
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6.3 K-Nearest Neighbors
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6.4 A Brief Tour of Other Classifiers
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6.5 Evaluating Classifiers
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6.6 The Challenge of Imbalanced Data
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6.7 Multi-Class Classification
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6.8 A Worked Example: Customer Churn Prediction
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6.9 Common Pitfalls in Classification
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7.1 Learning Without Labels
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7.2 K-Means Clustering
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7.3 Hierarchical Clustering
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7.4 Other Clustering Algorithms
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7.5 Dimensionality Reduction
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7.6 Anomaly Detection
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7.7 A Worked Example: Customer Segmentation
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7.8 Common Pitfalls in Unsupervised Learning
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8.1 Why Models Underperform
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8.2 The Bias-Variance Tradeoff
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8.3 Learning Curves: A Diagnostic Tool
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8.4 Cross-Validation in Depth
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8.5 Feature Engineering
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8.6 Hyperparameter Tuning
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8.7 Regularization Revisited
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8.8 Ensemble Methods
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8.9 A Diagnostic Workflow
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8.10 Common Pitfalls in Improving Performance
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9.1 Where Machine Learning Lives Today
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9.2 Healthcare
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9.3 Finance
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9.4 Recommendation Systems
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9.5 Natural Language Processing
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9.6 Computer Vision
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9.7 Time Series Forecasting
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9.8 Manufacturing and the Internet of Things
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9.9 Ethical Considerations
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9.10 Common Pitfalls in Real-World Applications
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10.1 From Model to Product
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10.2 The ML Lifecycle
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10.3 Deployment Architectures
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10.4 Serving Models
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10.5 Monitoring and Concept Drift
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10.6 A/B Testing and Rollout
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10.7 MLOps Practices
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10.8 Common Deployment Pitfalls
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10.9 Where to Go from Here
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