Qamar Usman (80 results)

Condition: good. Paperback Book.

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Condition: New. In English.

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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condition: new. Paperback. This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Condition: New. 2024th edition NO-PA16APR2015-KAP.

Condition: New.

Condition: New.

Condition: New.

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Hardcover. Condition: Very Good. 2. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.

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Condition: New. In English.

Condition: New.

Paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Buch. Condition: Neu. Neuware -This textbook covers the foundations, concepts, and implementations of Generative AI. It offers students with the knowledge and skills essential for the evolving field of Generative AI. It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. The implementation of each generative AI model is also part of the textbook.The textbook consists of four parts. Part 1 which consists of two chapters is an introduction to Generative AI. It provides an exploration of the fundamental concepts underlying the field of generative Artificial Intelligence. It builds a strong foundation for the reader to understand the remaining parts. In the Part 2, all the core concepts of Generative AI like Variational Autoencoders, Generative Adversarial Networks, Normalizing Flow Models Autoregressive Models, Energy-based and Diffusion Models and Large Language Models (LLMs) are covered. In Part 3, applications of Generative AI including World Models, Content Generation with Generative Models and Building Applications with LLMs are discussed. Finally, Part 4, looks at the ethical considerations for the development and deployment of Generative AI models. Chapter-wise complete source codes are delivered as an additional resource. Practical exercises are also included at the end of each chapter.The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The book is suitable for both undergraduate and graduate students in computer science and engineering. This textbook is a valuable resource for students who want to study the domain of Generative AI.…

Condition: New.

Hardcover. Condition: Brand New. 450 pages. 9.26x6.11x9.49 inches. In Stock.

Condition: New. In English.

Condition: New.

Buch. Condition: Neu. Neuware - This textbook covers the foundations, concepts, and implementations of Generative AI. It offers students with the knowledge and skills essential for the evolving field of Generative AI. It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. The implementation of each generative AI model is also part of the textbook.The textbook consists of four parts. Part 1 which consists of two chapters is an introduction to Generative AI. It provides an exploration of the fundamental concepts underlying the field of generative Artificial Intelligence. It builds a strong foundation for the reader to understand the remaining parts. In the Part 2, all the core concepts of Generative AI like Variational Autoencoders, Generative Adversarial Networks, Normalizing Flow Models Autoregressive Models, Energy-based and Diffusion Models and Large Language Models (LLMs) are covered. In Part 3, applications of Generative AI including World Models, Content Generation with Generative Models and Building Applications with LLMs are discussed. Finally, Part 4, looks at the ethical considerations for the development and deployment of Generative AI models. Chapter-wise complete source codes are delivered as an additional resource. Practical exercises are also included at the end of each chapter.The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The book is suitable for both undergraduate and graduate students in computer science and engineering. This textbook is a valuable resource for students who want to study the domain of Generative AI.…

Condition: Gut. Zustand: Gut | Seiten: 500 | Sprache: Englisch | Produktart: Bücher | 1. Introduction.- 2. Applications of Data Science.- 3. Widely Used Techniques in Data Science Applications.- 4. Data Preprocessing.- 5. Classification.- 6. Clustering.- 7. Text Mining.- 8. Deep Learning.- 9. Frequent Pattern Mining.- 10. Regression Analysis.- 11. Data Science Programming Language.- 12. Practical Data Science with WEKA. …

Condition: New. pp. XVI, 236 147 illus., 27 illus. in color. 2nd ed. 2019 edition NO-PA16APR2015-KAP.

Paperback. Condition: Brand New. 2nd edition. 498 pages. 9.25x6.10x1.01 inches. In Stock.

Condition: New.