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AI for Data Science

Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and;

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Smarter Data Science Succeeding with

science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI;

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Industry 4.0, AI, and Data Science

, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers;

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Data Science Crash Course for Beginners with Python

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Smart Data

contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning;

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Applying Data Science

intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem;

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Applying Data Science

intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem;

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Smart Data

contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning;

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Advances in Artificial Intelligence, Computation, and Data Science

: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and;

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Krunic:Succeeding with AI_p1

The big challenge for a successful AI project isn't deciding which problems you can solve. It's deciding which problems you should solve;

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Enabling AI Applications in Data Science

. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained;

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Big data ontrafeld (Printing-On-Demand). Neem betere zakelijke beslissingen met big data, data science en AI, David Stephenson, Hardcover

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Data Science on AWS

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on;

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Why AI/Data Science Projects Fail

illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners;

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Data Science

From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science;

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Applied Machine Learning for Health and Fitness

industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data;

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Analytical Skills for AI and Data Science Building Skills for an AIdriven Enterprise

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast;

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Artificial Intelligence for Business

easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters;

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Sustainable Intelligent Systems

This book discusses issues related to ICT, intelligent systems, data science, AI, machine learning, sustainable development and overall;

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Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare

processing rely on computer science's use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI;

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Practical AI for Healthcare Professionals

Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However;

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Hands-On Artificial Intelligence for IoT

challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial;

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Machine Learning and Artificial Intelligence for Agricultural Economics

This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards;

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Responsible AI

This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI;

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Responsible AI

This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI;

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Essentials of Data Science and Analytics

programming, and Python. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book;

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Ontological Engineering approach of developing Ontology of Information Science

level interoperation and information integration. They provide functional features for AI and knowledge representation. Domain Ontology is a;

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