The Artificial Intelligence and Machine Learning Suite provides a comprehensive, practical foundation in two of today’s most influential and rapidly evolving fields. Designed for professionals, educators, and lifelong learners, this bundled learning experience builds essential knowledge of key AI and machine learning concepts, applications, processes, and techniques—without requiring a technical background.

Through two complementary courses, you will gain a clear understanding of how artificial intelligence and machine learning work, where they are being applied, and how they are shaping business, technology, and the future of work. Together, these courses help you move from foundational AI concepts to a deeper understanding of how machines learn from data and improve decision-making over time.

Introduction to Artificial Intelligence

This course introduces the core forms of artificial intelligence and explores how we interact with AI as consumers and professionals through tools such as chatbots, recommendation engines, and analytics platforms. You will examine how AI is used in business, identify industries being transformed or disrupted by AI, and explore real-world applications including natural language processing, forecasting, and robotics.

You will also look “under the hood” to understand how computers learn through artificial neural networks and machine learning models, review the AI development and implementation process, consider the impact of AI on the workforce, and explore ethical considerations related to responsible AI deployment.

Introduction to Machine Learning

Building on AI foundations, this course focuses on how machine learning systems are designed to solve specific types of problems using data. You will explore supervised, unsupervised, and semi-supervised learning techniques, the types of data each requires, and the situations where each approach is most effective.

The course also examines offline and online training and prediction models, automated machine learning, and how cloud environments influence machine learning workflows. You will conclude by exploring emerging research areas that are shaping the future of machine learning innovation.