Hands-On Unsupervised Learning Using Python. How to Build Applied Machine Learning Solutions from Unlabeled Data Ankur A. Patel

Spis treści książki

• Preface
  • A Brief History of Machine Learning
  • AI Is Back, but Why Now?
  • The Emergence of Applied AI
  • Major Milestones in Applied AI over the Past 20 Years
  • From Narrow AI to AGI
  • Objective and Approach
  • Prerequisites
  • Roadmap
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
• I. Fundamentals of Unsupervised Learning
• 1. Unsupervised Learning in the Machine Learning Ecosystem
  • Basic Machine Learning Terminology
  • Rules-Based vs. Machine Learning
  • Supervised vs. Unsupervised
    • The Strengths and Weaknesses of Supervised Learning
    • The Strengths and Weaknesses of Unsupervised Learning
  • Using Unsupervised Learning to Improve Machine Learning Solutions
    • Insufficient labeled data
    • Overfitting
    • Curse of dimensionality
    • Feature engineering
    • Outliers
    • Data drift
  • A Closer Look at Supervised Algorithms
    • Linear Methods
      • Linear regression
      • Logistic regression
    • Neighborhood-Based Methods
      • k-nearest neighbors
    • Tree-Based Methods
      • Single decision tree
      • Bagging
      • Random forests
      • Boosting
    • Support Vector Machines
    • Neural Networks
  • A Closer Look at Unsupervised Algorithms
    • Dimensionality Reduction
      • Linear projection
        • Principal component analysis (PCA)
        • Singular value decomposition (SVD)
        • Random projection
      • Manifold learning
        • Isomap
        • t-distributed stochastic neighbor embedding (t-SNE)
        • Dictionary learning
      • Independent component analysis
      • Latent Dirichlet allocation
    • Clustering
      • k-means
      • Hierarchical clustering
      • DBSCAN
    • Feature Extraction
      • Autoencoders
      • Feature extraction using supervised training of feedforward networks
    • Unsupervised Deep Learning
      • Unsupervised pretraining
      • Restricted Boltzmann machines
      • Deep belief networks
      • Generative adversarial networks
    • Sequential Data Problems Using Unsupervised Learning
  • Reinforcement Learning Using Unsupervised Learning
  • Semisupervised Learning
  • Successful Applications of Unsupervised Learning
    • Anomaly Detection
      • Group segmentation
  • Conclusion
• 2. End-to-End Machine Learning Project
  • Environment Setup
    • Version Control: Git
    • Clone the Hands-On Unsupervised Learning Git Repository
    • Scientific Libraries: Anaconda Distribution of Python
    • Neural Networks: TensorFlow and Keras
    • Gradient Boosting, Version One: XGBoost
    • Gradient Boosting, Version Two: LightGBM
    • Clustering Algorithms
    • Interactive Computing Environment: Jupyter Notebook
  • Overview of the Data
  • Data Preparation
    • Data Acquisition
      • Download the data
      • Import the necessary libraries
      • Read the data
      • Preview the data
    • Data Exploration
      • Generate summary statistics
      • Identify nonnumerical values by feature
      • Identify distinct values by feature
    • Generate Feature Matrix and Labels Array
      • Create the feature matrix X and the labels array Y
      • Standardize the feature matrix X
    • Feature Engineering and Feature Selection
      • Check correlation of features
    • Data Visualization
  • Model Preparation
    • Split into Training and Test Sets
    • Select Cost Function
    • Create k-Fold Cross-Validation Sets
  • Machine Learning Models (Part I)
    • Model #1: Logistic Regression
      • Set hyperparameters
      • Train the model
      • Evaluate the results
  • Evaluation Metrics
    • Confusion Matrix
    • Precision-Recall Curve
    • Receiver Operating Characteristic
      • Evaluating the logistic regression model
  • Machine Learning Models (Part II)
    • Model #2: Random Forests
      • Set the hyperparameters
      • Train the model
      • Evaluate the results
    • Model #3: Gradient Boosting Machine (XGBoost)
      • Set the hyperparameters
      • Train the model
      • Evaluate the results
    • Model #4: Gradient Boosting Machine (LightGBM)
      • Set the hyperparameters
      • Train the model
      • Evaluate the results
  • Evaluation of the Four Models Using the Test Set
    • Logistic regression
    • Random forests
    • XGBoost gradient boosting
    • LightGBM gradient boosting
  • Ensembles
    • Stacking
      • Combine layer one predictions with the original training dataset
      • Set the hyperparameters
      • Train the model
      • Evaluate the results
  • Final Model Selection
  • Production Pipeline
  • Conclusion
• II. Unsupervised Learning Using Scikit-Learn
• 3. Dimensionality Reduction
  • The Motivation for Dimensionality Reduction
    • The MNIST Digits Database
      • Data acquisition and exploration
      • Load the MNIST datasets
      • Verify shape of datasets
      • Create Pandas DataFrames from the datasets
      • Explore the data
      • Display the images
  • Dimensionality Reduction Algorithms
    • Linear Projection vs. Manifold Learning
  • Principal Component Analysis
    • PCA, the Concept
    • PCA in Practice
      • Set the hyperparameters
      • Apply PCA
      • Evaluate PCA
      • Visualize the separation of points in space
    • Incremental PCA
    • Sparse PCA
    • Kernel PCA
  • Singular Value Decomposition
  • Random Projection
    • Gaussian Random Projection
    • Sparse Random Projection
  • Isomap
  • Multidimensional Scaling
  • Locally Linear Embedding
  • t-Distributed Stochastic Neighbor Embedding
  • Other Dimensionality Reduction Methods
  • Dictionary Learning
  • Independent Component Analysis
  • Conclusion
• 4. Anomaly Detection
  • Credit Card Fraud Detection
    • Prepare the Data
    • Define Anomaly Score Function
    • Define Evaluation Metrics
    • Define Plotting Function
  • Normal PCA Anomaly Detection
    • PCA Components Equal Number of Original Dimensions
    • Search for the Optimal Number of Principal Components
  • Sparse PCA Anomaly Detection
  • Kernel PCA Anomaly Detection
  • Gaussian Random Projection Anomaly Detection
  • Sparse Random Projection Anomaly Detection
  • Nonlinear Anomaly Detection
  • Dictionary Learning Anomaly Detection
  • ICA Anomaly Detection
  • Fraud Detection on the Test Set
    • Normal PCA Anomaly Detection on the Test Set
    • ICA Anomaly Detection on the Test Set
    • Dictionary Learning Anomaly Detection on the Test Set
  • Conclusion
• 5. Clustering
  • MNIST Digits Dataset
    • Data Preparation
  • Clustering Algorithms
  • k-Means
    • k-Means Inertia
    • Evaluating the Clustering Results
    • k-Means Accuracy
    • k-Means and the Number of Principal Components
    • k-Means on the Original Dataset
  • Hierarchical Clustering
    • Agglomerative Hierarchical Clustering
    • The Dendrogram
    • Evaluating the Clustering Results
  • DBSCAN
    • DBSCAN Algorithm
    • Applying DBSCAN to Our Dataset
    • HDBSCAN
  • Conclusion
• 6. Group Segmentation
  • Lending Club Data
    • Data Preparation
      • Load libraries
      • Explore the data
    • Transform String Format to Numerical Format
    • Impute Missing Values
    • Engineer Features
    • Select Final Set of Features and Perform Scaling
    • Designate Labels for Evaluation
  • Goodness of the Clusters
  • k-Means Application
  • Hierarchical Clustering Application
  • HDBSCAN Application
  • Conclusion
• III. Unsupervised Learning Using TensorFlow and Keras
• 7. Autoencoders
  • Neural Networks
    • TensorFlow
      • TensorFlow example
    • Keras
  • Autoencoder: The Encoder and the Decoder
  • Undercomplete Autoencoders
  • Overcomplete Autoencoders
  • Dense vs. Sparse Autoencoders
  • Denoising Autoencoder
  • Variational Autoencoder
  • Conclusion
• 8. Hands-On Autoencoder
  • Data Preparation
  • The Components of an Autoencoder
  • Activation Functions
  • Our First Autoencoder
    • Loss Function
    • Optimizer
    • Training the Model
    • Evaluating on the Test Set
  • Two-Layer Undercomplete Autoencoder with Linear Activation Function
    • Increasing the Number of Nodes
    • Adding More Hidden Layers
  • Nonlinear Autoencoder
  • Overcomplete Autoencoder with Linear Activation
  • Overcomplete Autoencoder with Linear Activation and Dropout
  • Sparse Overcomplete Autoencoder with Linear Activation
  • Sparse Overcomplete Autoencoder with Linear Activation and Dropout
  • Working with Noisy Datasets
  • Denoising Autoencoder
    • Two-Layer Denoising Undercomplete Autoencoder with Linear Activation
    • Two-Layer Denoising Overcomplete Autoencoder with Linear Activation
    • Two-Layer Denoising Overcomplete Autoencoder with ReLu Activation
  • Conclusion
• 9. Semisupervised Learning
  • Data Preparation
  • Supervised Model
  • Unsupervised Model
  • Semisupervised Model
  • The Power of Supervised and Unsupervised
  • Conclusion
• IV. Deep Unsupervised Learning Using TensorFlow and Keras
• 10. Recommender Systems Using Restricted Boltzmann Machines
  • Boltzmann Machines
    • Restricted Boltzmann Machines
  • Recommender Systems
    • Collaborative Filtering
    • The Netflix Prize
  • MovieLens Dataset
    • Data Preparation
    • Define the Cost Function: Mean Squared Error
    • Perform Baseline Experiments
  • Matrix Factorization
    • One Latent Factor
    • Three Latent Factors
    • Five Latent Factors
  • Collaborative Filtering Using RBMs
    • RBM Neural Network Architecture
    • Build the Components of the RBM Class
    • Train RBM Recommender System
  • Conclusion
• 11. Feature Detection Using Deep Belief Networks
  • Deep Belief Networks in Detail
  • MNIST Image Classification
  • Restricted Boltzmann Machines
    • Build the Components of the RBM Class
    • Generate Images Using the RBM Model
    • View the Intermediate Feature Detectors
  • Train the Three RBMs for the DBN
    • Examine Feature Detectors
    • View Generated Images
  • The Full DBN
    • How Training of a DBN Works
    • Train the DBN
  • How Unsupervised Learning Helps Supervised Learning
    • Generate Images to Build a Better Image Classifier
  • Image Classifier Using LightGBM
    • Supervised Only
    • Unsupervised and Supervised Solution
  • Conclusion
• 12. Generative Adversarial Networks
  • GANs, the Concept
    • The Power of GANs
  • Deep Convolutional GANs
  • Convolutional Neural Networks
  • DCGANs Revisited
    • Generator of the DCGAN
    • Discriminator of the DCGAN
    • Discriminator and Adversarial Models
    • DCGAN for the MNIST Dataset
  • MNIST DCGAN in Action
    • Synthetic Image Generation
  • Conclusion
• 13. Time Series Clustering
  • ECG Data
  • Approach to Time Series Clustering
    • k-Shape
  • Time Series Clustering Using k-Shape on ECGFiveDays
    • Data Preparation
    • Training and Evaluation
  • Time Series Clustering Using k-Shape on ECG5000
    • Data Preparation
    • Training and Evaluation
  • Time Series Clustering Using k-Means on ECG5000
  • Time Series Clustering Using Hierarchical DBSCAN on ECG5000
  • Comparing the Time Series Clustering Algorithms
    • Full Run with k-Shape
    • Full Run with k-Means
    • Full Run with HDBSCAN
    • Comparing All Three Time Series Clustering Approaches
  • Conclusion
• 14. Conclusion
  • Supervised Learning
  • Unsupervised Learning
    • Scikit-Learn
    • TensorFlow and Keras
  • Reinforcement Learning
  • Most Promising Areas of Unsupervised Learning Today
  • The Future of Unsupervised Learning
  • Final Words
• Index

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