The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics

Papp, Stefan; Weidinger, Wolfgang; Munro, Katherine; Cadonna, Annalisa; Langs, Georg; Licandro, Roxane; Meir-Huber, Mario; Nikoli?, Danko; Toth, Zoltan; Wazir, Rania; Zauner, Günther; Antosova Vesela, Barbora; Bruckmüller, Karin; Eder, Jana; Gorzala, Jeannette; Hahn, Gerald A.; Mata, Christian; McIntyre, Sean; György, Móra; Pasieska, Manuel; Rugli, Victoria

ISBN 10: 1569909342 / ISBN 13: 9781569909348

Edité par Hanser Publications, 2024

A propos de cet article

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Détails bibliographiques

• Titre: The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics
• Éditeur: Hanser Publications
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 1569909342
• ISBN à 13 chiffres: 9781569909348
• Reliure: Couverture rigide
• Édition: 2ème Édition
• État de l'article: like_new

A propos de ce titre

Synopsis

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.

À propos de l'auteur

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.