In this chapter, you will be introduced to the concept of Artificial Intelligence, its history, and its significance in today's world. You will learn about the different types of AI and their applications, providing you with a solid foundation for the rest of the course.

In this chapter, you will explore the fundamentals of Machine Learning, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. You will gain a deep understanding of these concepts and their applications, preparing you for the more advanced topics in the course.

In this chapter, you will be introduced to the concept of Federated Learning, its benefits, challenges, and differences compared to Centralized Learning. You will gain a solid understanding of the fundamentals of Federated Learning, setting the stage for the more advanced topics in the course.

In this chapter, you will explore various Federated Learning Algorithms, including Federated Averaging, Federated Percentage Splitting, and Federated Online Convex Optimization. You will learn how these algorithms work and their applications, enabling you to apply Federated Learning to real-world scenarios.

In this chapter, you will explore the various applications of Federated Learning in Mobile Devices, IoT Devices, Healthcare, and Finance. You will learn how Federated Learning is used in these industries and the benefits it provides, enabling you to apply your newfound knowledge to real-world scenarios.

In this final chapter, you will explore the future of Artificial Intelligence and Federated Learning, including emerging trends and applications, ethical considerations, and the role these technologies will play in shaping the future. You will gain a solid understanding of the potential impact of AI and Federated Learning on industries and society, preparing you for a rewarding career in this field.