Artificial Intelligence Information Resources

Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course. Topics include: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Are you an AI specialist, a data scientist, a business leader, or someone who would like to know more about how artificial intelligence is impacting our world? In this course, instructor Dr. Jonathan Reichental introduces you to a hot topic in AI development: the GPT-3 (Generative Pre-trained Transformer 3) technology released by Open AI. Reichental describes the increasingly important role that AI is playing in business. He covers a brief history of AI, then tells you about how OpenAI got started, its mission and vision, and its unique charter. Reichental discusses the core concepts of GPT-3, how it adds value, and a few of its challenges and limitations. He describes the future of AI, then concludes with suggestions for next steps you and your organization can take relative to GPT-3 and AI. Apply for this course

Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast. This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI. Topics include: The history of AI Machine learning Technical approaches to AI AI in robotics Integrating AI with big data Avoiding pitfalls Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Do you know how AI and machine learning affect you, your career path, and the world around us? This learning path teaches you the concepts and future directions of artificial intelligence and machine learning. You'll be able to make more informed decisions and contributions in your work environment. Gain an understanding of how AI and machine learning work. Discover how leading companies are using AI and ML. Learn how AI addresses accountability, security, and more. Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s. Topics include: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Artificial intelligence (AI) offers businesses the potential for a dramatic increase in functionality and profitability, but it can also spark an array of complex ethical, legal, and social challenges. In this nontechnical, conceptually oriented course, Barton Poulson digs into the hazards of AI, offering potential solutions to key concerns. Barton explores the ethical issues posed by AI, including competing concepts of fairness and moral reasoning. He also goes over social concerns and safety challenges for AI, such as potential life-and-death scenarios in autonomous driving. Barton concludes with recommendations tailored to developers, executives, PR professionals, regulators, and consumers to help them reap the potential of AI in a manner that's worthy of trust and profitable to all. Topics include: Review the challenges of AI. Apply narrow AI to a decision. Define two major approaches used when dealing with AI. Examine supervised and unsupervised learning. Explain harassment by AI. Identify three concepts that distributive justice is based on. Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Innovations in finance, health, robotics, and a variety of other sectors have been made possible with reinforcement learning (RL), which involves the training of machines to learn from their environment. Many top tech companies are investing heavily in this field. In this course, instructor Khaulat Abdulhakeem helps you learn the basics of this relatively new, but valuable skill. Get to know the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. Khaulat shows you how to define and represent reinforcement learning problems. She also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, she explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation. Topics include: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Ever wondered what’s really going on underneath a machine learning algorithm? The answer is linear algebra. Without it, machine learning can’t exist. Linear algebra is a prerequisite for understanding and creating nearly all machine learning algorithms, especially those that prop up neural networks, natural language processing tools, and deep learning models. Join instructor Terezija Semenski for an in-depth exploration of the core concepts of linear algebra alongside the techniques needed to design and implement a successful machine learning algorithm. Discover the basics of vector arithmetic, vector norms, matrix properties, advanced operations, matrix transformation, and algorithms like Google PageRank. By the end of this course, you’ll be ready to take the principles of linear algebra and apply them to your next big machine learning project. Topics include: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Image processing is a ubiquitous technology these days, used in just about every feature of smartphone cameras, in video games, social media apps, graphics software, and much more. Computer vision is the field of science that makes it possible for computers to not just capture images, but to process, analyze, and understand digital images. In this course, Eduardo Corpeño shows you how to use Python to write image processing operations of your own from scratch, to get a deeper understanding of the under-the-hood operations of image processing and popular tools like OpenCV. Eduardo shows you how to tap into the details of the algorithms behind image processing so you can understand what’s going on inside and make better use of these tools in the future. He also provides programming challenges so you can create your own implementations and compare your work with his solutions. Topics include: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course