The Handbook of Data Science and AI : Generate Value from Data with Machine Learning and Data Analytics
Language: English
Published by Hanser Fachbuchverlag Aug 2024, 2024
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - - A comprehensive overview of the various fields of application of data science and artificial intelligence.- Case studies from practice to make the described concepts tangible.- Practical examples to help you carry out simple data analysis projects.- BONUS in print edition: Elektronisches Buch insideData Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success.The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies.WHATS INSIDE //- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.- ML and AI in Production: Turning experimentation into a working data science product.- Presenting your Results: Essential presentation techniques for data scientists.…
Seller Inventory # 9781569909348
- Title
- The Handbook of Data Science and AI : Generate Value from Data with Machine Learning and Data Analytics
- Author
- Katherine Munro
- Publisher
- Hanser Fachbuchverlag Aug 2024
- Publication year
- 2024
- Condition
- Neu
- Binding
- Kombiprodukt
- Language
- English
- ISBN 10
- 1569909342
- ISBN 13
- 9781569909348
- Edition
- 2nd Edition
- Item weight
- 1,730 grams
- Dimensions
- 245x185x57 mm
Data Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them:
- Understand crucial data science concepts, from statistics and mathematics to legal and ethical considerations.
- Learn how to build data platforms and deploy safe and robust data projects to production.
- Gain the vocabulary to communicate technical requirements and roadmaps to diverse business stakeholders.
- Dive into practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.
The team of authors consists of data experts from business and academia, including data scientists, engineers, business leaders and legal experts. Their broad, deep guide to all aspects of working with data and AI includes:
- Machine Learning Fundamentals: Foundations of mathematics and statistics, plus common frameworks for applying ML in practice: from statistical ML to neural networks, Transformers and AutoML
- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, and put it to use in real world applications
- Foundation Models and Generative AI: Understand the strengths, challenges, and practical considerations for working with generative models for text, image, video, and other types of data
- Modeling and Simulation: Model the behavior of complex systems and do a What-If analysis covering different scenarios
- Data Science, ML and AI in production: How can you use cloud and database technologies and MLOps to turn experimentation into a working data product?
- Talking about Data: Communication and presentation skills for effective data teams and innovative business leaders
- Building Safe, Responsible AI: Best practices in ML Security; safeguarding generative AI models from attack; and how to adhere to GDPR, CCPA, and the new AI act
All authors are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and Machine Learning and raising awareness of the opportunities and potential risks of these technologies.
"Synopsis" may belong to another edition of this title.
About the Author
The team of authors consists of data experts from business and academia. The spectrum ranges from executives to data engineers who create production systems, to data scientists who generate value from data. All authors are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and Machine Learning and raising awareness of the opportunities and potential risks of these technologies.
"About the title" may belong to another edition of this title.
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