This exam preparation booklet is a comprehensive guide designed to help you earn your certification for the NL AIC AI Fundamentals (AI Brevet) and AI Basis. It can also be used for exams based on the EXIN BCS Artificial Intelligence Essentials and/or Foundation. For those focusing on the Artificial Intelligence Essentials, sections 1.1 and 2.1 are relevant, while all sections apply to the Artificial Intelligence Foundation. All the knowledge areas described in the preparation guide will be covered with exam-like questions. The number of questions per topic can differ, depending on the weights used in the formal exam requirements. The booklet is structured into two main sections: The first part features questions without answers, allowing you to test your knowledge and identify areas for improvement. The second part provides the correct answers along with concise explanations to enhance your understanding. This exam preparation booklet will help prepare you for various acknowledges AI certification exams and provides you with sertanty going in to the exam session.

This exam preparation booklet is a comprehensive guide designed to help you earn your certification for the NL AIC AI Fundamentals (AI Brevet) and AI Basis. It can also be used for exams based on the EXIN BCS Artificial Intelligence Essentials and/or Foundation. For those focusing on the Artificial Intelligence Essentials, sections 1.1 and 2.1 are relevant, while all sections apply to the Artificial Intelligence Foundation. All the knowledge areas described in the preparation guide will be covered with exam-like questions. The number of questions per topic can differ, depending on the weights used in the formal exam requirements. The booklet is structured into two main sections: The first part features questions without answers, allowing you to test your knowledge and identify areas for improvement. The second part provides the correct answers along with concise explanations to enhance your understanding. This exam preparation booklet will help prepare you for various acknowledges AI certification exams and provides you with sertanty going in to the exam session.

Update: 8/11/2020 The author received notice that he passed the Microsoft AI Fundamentals exam AI-900. This was the study guide he developed to prepare for the exam. David Voss, Azure AI Fundamentals AI-900, Microsoft Certification ID: 990151288 Audience This study guide follows the syllabus for the Microsoft AI Foundations exam (AI-900). More importantly, this book will help you gain the foundational knowledge needed to become an AI practitioner. You do not need a mathematical or programming background to understand the concepts in this book or to pass the AI-900 exam. About VOSS AIThe motto of VOSS.AI is "AI for All." VOSS.AI creates products and services for anyone who has an interest in learning about Artificial Intelligence. We have chosen Microsoft AI as our platform of choice because Microsoft has made a concerted effort to ensure their AI products are accessible to everyone. Study with Confidence We are committed to the integrity of the exams, as well as you as a student. This study guide does not contain any material that compromises the integrity of any Microsoft exam. All materials, including practice questions, were developed using the syllabus for the exam and thorough research of published articles. Additional Online Resources VOSS.AI provides you with additional online resources for your studies. Specifically, you can find additional study questions for the AI-900 exam. We will add new questions frequently.

Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft AI-900 Microsoft Azure AI Fundamentals certification exam. Exam Ref AI-900 Microsoft Azure AI Fundamentals offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to demonstrate real-world mastery of common machine learning (ML) and artificial intelligence (AI) workloads and how to use them in Azure.

AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.

The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala