ISBN: 9781032268286 - machine learning: concepts, techniques and applications (18 risultati)

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Hardback. Condizione: New. Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms - When to use them and Why - for Application developers and Researchers Machine Learning from an Application Perspective - General and Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning ExercisesThe book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.…

Hardcover. Condizione: Brand New. 448 pages. 10.00x7.00x1.26 inches. In Stock.

Hardback. Condizione: New. Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms - When to use them and Why - for Application developers and Researchers Machine Learning from an Application Perspective - General and Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning ExercisesThe book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.…

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. T V Geetha is a retired Senior Professor of Computer Science and Engineering with over 35 years of teaching experience in the areas of Artificial Intelligence, Machine Learning, Natural Language Processing and Information Retrieval. Her .…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms - When to use them & Why - for Application developers and Researchers Machine Learning from an Application Perspective - General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation. 480 pp. Englisch.…

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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms - When to use them & Why - for Application developers and Researchers Machine Learning from an Application Perspective - General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.…