In this module, you will learn the definition of AI and the basics of generative AI. You will explore the impact, applications, and use cases of AI and how it is transforming our lives. Additionally, you will discover common tools and applications of generative AI. You will learn about AI agents and smart assistants and their common use cases. Finally, you will examine how AI is reshaping various industries and sectors.

In this module, you will learn about the core concepts of AI, covering deep learning, machine learning, and neural networks. You’ll explore generative AI models, including large language models (LLMs), and their capabilities. Additionally, you’ll explore the development and application of AI in various domains like natural language processing (NLP), computer vision, and robotics, discovering how advancements in these areas drive diverse innovations and use cases.

In this module, you will explore the development of AI in business-relevant domains, including AI agents and robotics. You will discover the transformative impact of AI, especially generative AI, on business operations. You'll learn about the benefits AI offers to organizations and examine various use cases. Additionally, you'll discover how AI can revolutionize your work and work environment. The module also covers career opportunities in the AI domain. You'll examine key considerations for organizations looking to adopt AI and learn different frameworks and approaches used for AI implementation. This includes an in-depth look at IBM's AI Ladder framework.

This module focuses on AI ethics and governance and addresses prevalent concerns and issues surrounding the AI landscape. In this module, you’ll learn about the fundamental principles of AI ethics and how to implement them. The module also discusses the specific ethical considerations related to generative AI. You will also learn how AI ethics are enforced through governance and regulations. This module includes a final project and a graded quiz to assess your understanding of the course concepts.

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction.

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn.

Deploy machine learning algorithms and pipelines on Apache Spark.

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow.