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

✍ Scribed by Vasques, Xavier;

• Year: 2024
• Tongue: English
• Leaves: 512
• Category: Library

✦ Synopsis

Machine Learning Theory and ApplicationsEnables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries
Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps).
Additional topics covered inMachine Learning Theory and Applications include
Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more
Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs)
Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data
Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications
Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.

✦ Table of Contents

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
End User License Agreement