Machine Learning Theory and Applications : Hands-on Use Cases with Python on Classical and Quantum Machines

Cover
Table of Contents
Title Page
Copyright Page
Dedication Page
Foreword
Acknowledgments
General Introduction
   The Birth of the Artificial Intelligence Concept
   Machine Learning
   From Theory to Production
1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning
   1.1 Learning Styles for Machine Learning
   1.2 Essential Python Tools for Machine Learning
   1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs
   1.4 Where to Find the Datasets and Code Examples
   Further Reading
2 Feature Engineering Techniques in Machine Learning
   2.1 Feature Rescaling: Structured Continuous Numeric Data
   2.2 Strategies to Work with Categorical (Discrete) Data
   2.3 Time‐Related Features Engineering
   2.4 Handling Missing Values in Machine Learning
   2.5 Feature Extraction and Selection
   Further Reading
3 Machine Learning Algorithms
   3.1 Linear Regression
   3.2 Logistic Regression
   3.3 Support Vector Machine
   3.4 Artificial Neural Networks
   3.5 Many More Algorithms to Explore
   3.6 Unsupervised Machine Learning Algorithms
   3.7 Machine Learning Algorithms with HephAIstos
   References
   Further Reading
4 Natural Language Processing
   4.1 Classifying Messages as Spam or Ham
   4.2 Sentiment Analysis
   4.3 Bidirectional Encoder Representations from Transformers
   4.4 BERT’s Functionality
   4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow
   4.6 Utilizing BERT for Text Summarization
   4.7 Utilizing BERT for Question Answering
   Further Reading
5 Machine Learning Algorithms in Quantum Computing
   5.1 Quantum Machine Learning
   5.2 Quantum Kernel Machine Learning
   5.3 Quantum Kernel Training
   5.4 Pegasos QSVC: Binary Classification
   5.5 Quantum Neural Networks
   5.6 Quantum Generative Adversarial Network
   5.7 Quantum Algorithms with HephAIstos
   References
   Further Reading
6 Machine Learning in Production
   6.1 Why Use Docker Containers for Machine Learning?
   6.2 Machine Learning Prediction in Real Time Using Docker and Python REST APIs with Flask
   6.3 From DevOps to MLOPS: Integrate Machine Learning Models Using Jenkins and Docker
   6.4 Machine Learning with Docker and Kubernetes: Install a Cluster from Scratch
   6.5 Machine Learning with Docker and Kubernetes: Training Models
   6.6 Machine Learning with Docker and Kubernetes: Batch Inference
   6.7 Machine Learning Prediction in Real Time Using Docker, Python Rest APIs with Flask, and Kubernetes: Online Inference
   6.8 A Machine Learning Application that Deploys to the IBM Cloud Kubernetes Service: Python, Docker, Kubernetes
   6.9 Red Hat OpenShift to Develop and Deploy Enterprise ML/DL Applications
   6.10 Deploying a Machine Learning Model as an API on the Red Hat OpenShift Container Platform: From Source Code in a GitHub Repository with Flask, Scikit‐Learn, and Docker
   Further Reading
Conclusion: The Future of Computing for Data Science?
   Binary Systems
   Biologically Inspired Systems
   Quantum Systems: Qubits
   Final Thoughts
   Further Reading
Index
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