Machine learning algorithms applications (233 Ergebnisse)

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paperback. Zustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

Paperback. Zustand: New. New paperback. An unread/unopened copy. May have VERY minor rubbing and scuffing from shelf wear.; 100% Satisfaction Guaranteed! Ships same or next business day.

Hardback. Zustand: Good. This book provides a fresh, intuitive approach to machine learning, detailing the fundamental concepts necessary for building projects and conducting research. With colour illustrations, practical real-world examples, and MATLAB-based exercises, it is an essential resource for students and an ideal reference for researchers and practitioners in the field.…

hardcover. Zustand: Very Good.

hardcover. Zustand: Fine.

Zustand: Very Good. Zustand des Schutzumschlags: 31119220. 1793006852. 10/26/2026 9:27:32 AM.

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hardcover. Zustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

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Paperback. Zustand: Brand New. 82 pages. 9.00x6.00x0.17 inches. In Stock.

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Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In good all round condition. Dust jacket in good condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,850grams, ISBN:9781107123526.…

Hardcover. Zustand: Very Good. 1st ed. 2023. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

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Hardcover. Zustand: Very Good. In stock ready to dispatch from the UK.

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Taschenbuch. Zustand: Neu. Neuware - Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources explores how AI techniques enhance renewable energy systems through optimized power electronics. This essential guide introduces machine learning applications for improving control, predictive maintenance, and fault detection across renewable sources like solar and wind. With real-world case studies and practical insights, the book equips engineers, researchers, and students with tools to tackle challenges in renewable integration, power quality, and system reliability. An invaluable resource for both beginners and experts, this book sheds light on AI-driven innovations shaping a sustainable energy future.…

Paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Zustand: Neu. Neuware - THE FOUNDATIONAL GUIDE TO MACHINE LEARNINGA Practical Introduction to Core Concepts, Algorithms, Data, and Real World Applications>BACKGROUND OF THIS BOOK>ADVANTAGES OF BUYING THIS BOOKLearn from beginner to confident practitioner with a structured learning path.Master core machine learning concepts and algorithms without unnecessary complexity.Understand data preparation, model training, validation, testing, and evaluation.Explore practical workflows that connect machine learning concepts with real-world applications.Learn responsible machine learning practices, including fairness, privacy, security, transparency, and human oversight.>CONTENTS OF THIS BOOKInside this comprehensive guide, you will discover: Machine Learning FundamentalsData Types, Quality, Sampling, and GovernanceData Preparation and ExplorationFeatures, Representation, Scaling, and Feature EngineeringSupervised Learning, Regression, and ClassificationDecision Trees, Random Forests, and BoostingUnsupervised Learning and ClusteringDimensionality Reduction and VisualizationNeural Networks and Deep LearningTraining, Validation, Testing, and Cross-ValidationEvaluation Metrics and Model ComparisonOverfitting, Underfitting, and GeneralizationPractical Machine Learning WorkflowsDeployment, Monitoring, Data Drift, and MaintenanceResponsible and Ethical Machine LearningReal-World Applications and Case StudiesBuilding Your Own Machine Learning Learning Path>ENCOURAGEMENT FOR THE READERS>Read each chapter carefully, practice what you learn, reflect on the concepts, and continue developing your skills. With consistent learning and practical application, you can build the knowledge and confidence needed to understand machine learning and explore its many possibilities.Your journey into machine learning starts here.…

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