The Supervised Learning Workshop - Second Edition

Spis treści książki

• The Supervised Learning Workshop
• Second Edition
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Installation and Setup
    • Installing Anaconda on Windows
    • Installing Anaconda on macOS
    • Installing Anaconda on Linux
    • Installing Libraries
    • Accessing the Code Files
• 1. Fundamentals
  • Introduction
    • When to Use Supervised Learning
    • Python Packages and Modules
    • Loading Data in Pandas
    • Exercise 1.01: Loading and Summarizing the Titanic Dataset
    • Exercise 1.02: Indexing and Selecting Data
    • Exercise 1.03: Advanced Indexing and Selection
    • Pandas Methods
    • Exercise 1.04: Using the Aggregate Method
    • Quantiles
    • Lambda Functions
    • Exercise 1.05: Creating Lambda Functions
  • Data Quality Considerations
    • Managing Missing Data
    • Class Imbalance
    • Low Sample Size
    • Activity 1.01: Implementing Pandas Functions
  • Summary
• 2. Exploratory Data Analysis and Visualization
  • Introduction
  • Exploratory Data Analysis (EDA)
  • Summary Statistics and Central Values
    • Exercise 2.01: Summarizing the Statistics of Our Dataset
  • Missing Values
    • Finding Missing Values
    • Exercise 2.02: Visualizing Missing Values
    • Imputation Strategies for Missing Values
    • Exercise 2.03: Performing Imputation Using Pandas
    • Exercise 2.04: Performing Imputation Using Scikit-Learn
    • Exercise 2.05: Performing Imputation Using Inferred Values
    • Activity 2.01: Summary Statistics and Missing Values
  • Distribution of Values
    • Target Variable
    • Exercise 2.06: Plotting a Bar Chart
    • Categorical Data
    • Exercise 2.07: Identifying Data Types for Categorical Variables
    • Exercise 2.08: Calculating Category Value Counts
    • Exercise 2.09: Plotting a Pie Chart
    • Continuous Data
      • Skewness
      • Kurtosis
    • Exercise 2.10: Plotting a Histogram
    • Exercise 2.11: Computing Skew and Kurtosis
    • Activity 2.02: Representing the Distribution of Values Visually
  • Relationships within the Data
    • Relationship between Two Continuous Variables
      • Pearsons Coefficient of Correlation
    • Exercise 2.12: Plotting a Scatter Plot
    • Exercise 2.13: Plotting a Correlation Heatmap
      • Using Pairplots
    • Exercise 2.14: Implementing a Pairplot
    • Relationship between a Continuous and a Categorical Variable
    • Exercise 2.15: Plotting a Bar Chart
    • Exercise 2.16: Visualizing a Box Plot
    • Relationship Between Two Categorical Variables
    • Exercise 2.17: Plotting a Stacked Bar Chart
    • Activity 2.03: Relationships within the Data
  • Summary
• 3. Linear Regression
  • Introduction
  • Regression and Classification Problems
    • The Machine Learning Workflow
      • Business Understanding
      • Data Understanding
      • Data Preparation
      • Modeling
      • Evaluation
      • Deployment
    • Exercise 3.01: Plotting Data with a Moving Average
    • Activity 3.01: Plotting Data with a Moving Average
  • Linear Regression
    • Least Squares Method
    • The Scikit-Learn Model API
    • Exercise 3.02: Fitting a Linear Model Using the Least Squares Method
    • Activity 3.02: Linear Regression Using the Least Squares Method
    • Linear Regression with Categorical Variables
    • Exercise 3.03: Introducing Dummy Variables
    • Activity 3.03: Dummy Variables
    • Polynomial Models with Linear Regression
    • Exercise 3.04: Polynomial Models with Linear Regression
    • Activity 3.04: Feature Engineering with Linear Regression
    • Generic Model Training
    • Gradient Descent
    • Exercise 3.05: Linear Regression with Gradient Descent
    • Exercise 3.06: Optimizing Gradient Descent
    • Activity 3.05: Gradient Descent
  • Multiple Linear Regression
    • Exercise 3.07: Multiple Linear Regression
  • Summary
• 4. Autoregression
  • Introduction
  • Autoregression Models
    • Exercise 4.01: Creating an Autoregression Model
    • Activity 4.01: Autoregression Model Based on Periodic Data
  • Summary
• 5. Classification Techniques
  • Introduction
  • Ordinary Least Squares as a Classifier
    • Exercise 5.01: Ordinary Least Squares as a Classifier
  • Logistic Regression
    • Exercise 5.02: Logistic Regression as a Classifier Binary Classifier
    • Exercise 5.03: Logistic Regression Multiclass Classifier
    • Activity 5.01: Ordinary Least Squares Classifier Binary Classifier
      • Select K Best Feature Selection
    • Exercise 5.04: Breast Cancer Diagnosis Classification Using Logistic Regression
  • Classification Using K-Nearest Neighbors
    • Exercise 5.05: KNN Classification
    • Exercise 5.06: Visualizing KNN Boundaries
    • Activity 5.02: KNN Multiclass Classifier
  • Classification Using Decision Trees
    • Exercise 5.07: ID3 Classification
      • Classification and Regression Tree
    • Exercise 5.08: Breast Cancer Diagnosis Classification Using a CART Decision Tree
    • Activity 5.03: Binary Classification Using a CART Decision Tree
  • Artificial Neural Networks
    • Exercise 5.09: Neural Networks Multiclass Classifier
    • Activity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks
  • Summary
• 6. Ensemble Modeling
  • Introduction
  • One-Hot Encoding
    • Exercise 6.01: Importing Modules and Preparing the Dataset
  • Overfitting and Underfitting
    • Underfitting
    • Overfitting
    • Overcoming the Problem of Underfitting and Overfitting
  • Bagging
  • Bootstrapping
    • Exercise 6.02: Using the Bagging Classifier
    • Random Forest
    • Exercise 6.03: Building the Ensemble Model Using Random Forest
  • Boosting
    • Adaptive Boosting
    • Exercise 6.04: Implementing Adaptive Boosting
    • Gradient Boosting
    • Exercise 6.05: Implementing GradientBoostingClassifier to Build an Ensemble Model
  • Stacking
    • Exercise 6.06: Building a Stacked Model
    • Activity 6.01: Stacking with Standalone and Ensemble Algorithms
  • Summary
• 7. Model Evaluation
  • Introduction
  • Importing the Modules and Preparing Our Dataset
  • Evaluation Metrics
    • Regression Metrics
    • Exercise 7.01: Calculating Regression Metrics
    • Classification Metrics
      • Numerical Metrics
      • Curve Plots
    • Exercise 7.02: Calculating Classification Metrics
  • Splitting a Dataset
    • Hold-Out Data
    • K-Fold Cross-Validation
    • Sampling
    • Exercise 7.03: Performing K-Fold Cross-Validation with Stratified Sampling
  • Performance Improvement Tactics
    • Variation in Train and Test Errors
      • Learning Curve
      • Validation Curve
    • Hyperparameter Tuning
    • Exercise 7.04: Hyperparameter Tuning with Random Search
    • Feature Importance
    • Exercise 7.05: Feature Importance Using Random Forest
    • Activity 7.01: Final Test Project
  • Summary
• Appendix
  • 1. Fundamentals
    • Activity 1.01: Implementing Pandas Functions
  • 2. Exploratory Data Analysis and Visualization
    • Activity 2.01: Summary Statistics and Missing Values
    • Activity 2.02: Representing the Distribution of Values Visually
    • Activity 2.03: Relationships within the Data
  • 3. Linear Regression
    • Activity 3.01: Plotting Data with a Moving Average
    • Activity 3.02: Linear Regression Using the Least Squares Method
    • Activity 3.03: Dummy Variables
    • Activity 3.04: Feature Engineering with Linear Regression
    • Activity 3.05: Gradient Descent
  • 4. Autoregression
    • Activity 4.01: Autoregression Model Based on Periodic Data
  • 5. Classification Techniques
    • Activity 5.01: Ordinary Least Squares Classifier Binary Classifier
    • Activity 5.02: KNN Multiclass Classifier
    • Activity 5.03: Binary Classification Using a CART Decision Tree
    • Activity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks
  • 6. Ensemble Modeling
    • Activity 6.01: Stacking with Standalone and Ensemble Algorithms
  • 7. Model Evaluation
    • Activity 7.01: Final Test Project

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