Machine Learning Theory and Applications - by Xavier Vasques (Hardcover)

About the Book

"Machine learning (ML) and quantum computing are two technologies that have the potential to allow us to solve complex, previously impossible problems and help speed up areas such as model training or pattern recognition. The future of computing will certainly be comprised of classical, biologically inspired, and quantum computing. The intersection between quantum computing and AI/ML has received considerable attention in recent years and has enabled the development of quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, variational quantum classifiers or quantum generative adversarial networks (qGANs)."--

Book Synopsis

Machine Learning Theory and Applications

Enables 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 in Machine 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.

From the Back Cover

Enables 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 in Machine 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.

About the Author

Xavier Vasques, PhD, is the Chief Technology Officer of IBM Technology (France) and Distinguished Data Scientist at IBM. He currently holds the chair of cognitive sciences and technologies at the École National Supérieure de Cognitique located in the University of Bordeaux, France and he is member of the scientific council of the École des Mines d'Alès, France. He is a mathematician and head of the Clinical Neuroscience Research Laboratory based in Montpellier (France).