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Foreword
Preface
Part I. Machine Learning with Scikit-Learn
1. Machine Learning
What Is Machine Learning?
Machine Learning Versus Artificial Intelligence
Supervised Versus Unsupervised Learning
Unsupervised Learning with k-Means Clustering
Applying k-Means Clustering to Customer Data
Segmenting Customers Using More Than Two Dimensions
Supervised Learning
k-Nearest Neighbors
Using k-Nearest Neighbors to Classify Flowers
Summary
2. Regression Models
Linear Regression
Decision Trees
Random Forests
Gradient-Boosting Machines
Support Vector Machines
Accuracy Measures for Regression Models
Using Regression to Predict Taxi Fares
Summary
3. Classification Models
Logistic Regression
Accuracy Measures for Classification Models
Categorical Data
Binary Classification
Classifying Passengers Who Sailed on the Titanic
Detecting Credit Card Fraud
Multiclass Classification
Building a Digit Recognition Model
Summary
4. Text Classification
Preparing Text for Classification
Sentiment Analysis
Naive Bayes
Spam Filtering
Recommender Systems
Cosine Similarity
Building a Movie Recommendation System
Summary
5. Support Vector Machines
How Support Vector Machines Work
Kernels
Kernel Tricks
Hyperparameter Tuning
Data Normalization
Pipelining
Using SVMs for Facial Recognition
Summary
6. Principal Component Analysis
Understanding Principal Component Analysis
Filtering Noise
Anonymizing Data
Visualizing High-Dimensional Data
Anomaly Detection
Using PCA to Detect Credit Card Fraud
Using PCA to Predict Bearing Failure
Multivariate Anomaly Detection
Summary
7. Operationalizing Machine Learning Models
Consuming a Python Model from a Python Client
Versioning Pickle Files
Consuming a Python Model from a C# Client
Containerizing a Machine Learning Model
Using ONNX to Bridge the Language Gap
Building ML Models in C# with ML.NET
Sentiment Analysis with ML.NET
Saving and Loading ML.NET Models
Adding Machine Learning Capabilities to Excel
Summary
Part II. Deep Learning with Keras and TensorFlow
8. Deep Learning
Understanding Neural Networks
Training Neural Networks
Summary
9. Neural Networks
Building Neural Networks with Keras and TensorFlow
Sizing a Neural Network
Using a Neural Network to Predict Taxi Fares
Binary Classification with Neural Networks
Making Predictions
Training a Neural Network to Detect Credit Card Fraud
Multiclass Classification with Neural Networks
Training a Neural Network to Recognize Faces
Dropout
Saving and Loading Models
Keras Callbacks
Summary
10. Image Classification with Convolutional Neural Networks
Understanding CNNs
Using Keras and TensorFlow to Build CNNs
Training a CNN to Recognize Arctic Wildlife
Pretrained CNNs
Using ResNet50V2 to Classify Images
Transfer Learning
Using Transfer Learning to Identify Arctic Wildlife
Data Augmentation
Image Augmentation with ImageDataGenerator
Image Augmentation with Augmentation Layers
Applying Image Augmentation to Arctic Wildlife
Global Pooling
Audio Classification with CNNs
Summary
11. Face Detection and Recognition
Face Detection
Face Detection with Viola-Jones
Using the OpenCV Implementation of Viola-Jones
Face Detection with Convolutional Neural Networks
Extracting Faces from Photos
Facial Recognition
Applying Transfer Learning to Facial Recognition
Boosting Transfer Learning with Task-Specific Weights
ArcFace
Putting It All Together: Detecting and Recognizing Faces in Photos
Handling Unknow