Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically

Cover
Copyright
Table of Contents
Foreword
Preface
	Who Should Read This Book
	Why I Wrote This Book
	Running the Book’s Code Samples
	Navigating This Book
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Part I. Machine Learning with Scikit-Learn
	Chapter 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
	Chapter 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
	Chapter 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
	Chapter 4. Text Classification
		Preparing Text for Classification
		Sentiment Analysis
		Naive Bayes
		Spam Filtering
		Recommender Systems
			Cosine Similarity
			Building a Movie Recommendation System
		Summary
	Chapter 5. Support Vector Machines
		How Support Vector Machines Work
			Kernels
			Kernel Tricks
		Hyperparameter Tuning
		Data Normalization
		Pipelining
		Using SVMs for Facial Recognition
		Summary
	Chapter 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
	Chapter 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
	Chapter 8. Deep Learning
		Understanding Neural Networks
		Training Neural Networks
		Summary
	Chapter 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
	Chapter 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
	Chapter 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 Unknown Faces: Closed-Set Versus Open-Set Classification
		Summary
	Chapter 12. Object Detection
		R-CNNs
		Mask R-CNN
		YOLO
		YOLOv3 and Keras
		Custom Object Detection
			Training a Custom Object Detection Model with the Custom Vision Service
			Using the Exported Model
		Summary
	Chapter 13. Natural Language Processing
		Text Preparation
		Word Embeddings
		Text Classification
			Automating Text Vectorization
			Using TextVectorization in a Sentiment Analysis Model
			Factoring Word Order into Predictions
			Recurrent Neural Networks (RNNs)
			Using Pretrained Models to Classify Text
		Neural Machine Translation
			LSTM Encoder-Decoders
			Transformer Encoder-Decoders
			Building a Transformer-Based NMT Model
			Using Pretrained Models to Translate Text
		Bidirectional Encoder Representations from Transformers (BERT)
			Building a BERT-Based Question Answering System
			Fine-Tuning BERT to Perform Sentiment Analysis
		Summary
	Chapter 14. Azure Cognitive Services
		Introducing Azure Cognitive Services
			Keys and Endpoints
			Calling Azure Cognitive Services APIs
			Azure Cognitive Services Containers
		The Computer Vision Service
		The Language Service
		The Translator Service
		The Speech Service
		Putting It All Together: Contoso Travel
		Summary
Index
About the Author
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