AI and Machine Learning Algorithms: From Foundations to LLMs

Understanding how artificial intelligence and machine learning actually work is no longer just for research scientists. Navigating the landscape of modern algorithms—from classic regression to generative language models—is essential for solving complex business challenges. This text-based course guides you through the core paradigms of AI and machine learning, helping you transition from a curious beginner to a confident practitioner. You will gain a clear conceptual understanding of how algorithms learn from data and how to apply these techniques to real-world scenarios. What you'll learn: - Understand the fundamental differences between supervised, unsupervised, and reinforcement learning. - Explore deep learning neural networks and how they power modern computer vision and natural language processing. - Analyze the architecture of pre-trained large language models (LLMs) and basic prompt engineering principles. - Learn the mechanics of retrieval-augmented generation (RAG) and vector databases for enhancing AI applications. - Evaluate the strengths, limitations, and ethical considerations of different ML models for business decision-making. The course begins with foundational terminology and basic concepts before moving into practical algorithm breakdowns and modern generative AI workflows. Through written explanations and illustrative code snippets, you will build a solid framework for choosing the right AI approach for any problem. This course is designed entirely for beginners, with no prior background in advanced mathematics or data science required. Start reading today to demystify artificial intelligence and build a strong foundation in machine learning.