{"product_id":"machine-learning-theory-and-applications-hands-on-use-cases-with-python-on-classical-and-quantum-machines-hardcover","title":"Machine Learning Theory and Applications: Hands-On Use Cases with Python on Classical and Quantum Machines - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eXavier Vasques\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMachine Learning Theory and Applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEnables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning Theory and Applications\u003c\/i\u003e 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). \u003c\/p\u003e\u003cp\u003eAdditional topics covered in\u003ci\u003e Machine Learning Theory and Applications\u003c\/i\u003e include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCurrent use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more\u003c\/li\u003e \u003cli\u003eClassical 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)\u003c\/li\u003e \u003cli\u003eDifferent ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data\u003c\/li\u003e \u003cli\u003eFeature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003ci\u003eMachine Learning Theory and Applications\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEnables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning Theory and Applications\u003c\/i\u003e 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).\u003c\/p\u003e \u003cp\u003eAdditional topics covered in\u003ci\u003e Machine Learning Theory and Applications\u003c\/i\u003e include: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eCurrent use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more\u003c\/li\u003e \u003cli\u003eClassical 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)\u003c\/li\u003e \u003cli\u003eDifferent ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data\u003c\/li\u003e \u003cli\u003eFeature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning Theory and Applications\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eXavier Vasques, \u003c\/b\u003e 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).\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 512\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.13 x 11 x 8.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 31, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52494103314739,"sku":"9781394220618","price":136.53,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/s9XAWP48HJ9781394220618.webp?v=1759971284","url":"https:\/\/www.vysn.com\/en-ca\/products\/machine-learning-theory-and-applications-hands-on-use-cases-with-python-on-classical-and-quantum-machines-hardcover","provider":"VYSN","version":"1.0","type":"link"}