The AI Fundamentals course provides a comprehensive introduction to the essential concepts and techniques that power artificial intelligence. This course is designed for beginners, covering the foundational theories of AI, including machine learning, neural networks, and natural language processing. Participants will learn about the history of AI, its evolution, and its key components. The course also explores the ethical implications of AI, highlighting how it influences various sectors such as healthcare, finance, and education. By the end of the course, learners will have a solid understanding of how AI systems function and their potential to solve real-world problems.

A core component of the AI Fundamentals course is understanding the principles behind machine learning algorithms. This section covers supervised learning, unsupervised learning, and reinforcement learning, explaining how each method is applied to solve different types of problems. Students will gain insights into popular algorithms such as linear regression, decision trees, and clustering techniques. Practical exercises will help learners understand how to implement these algorithms using Python and popular AI libraries like Scikit-learn. By mastering these algorithms, participants will be equipped to build AI models for a variety of applications, including prediction, classification, and pattern recognition.