Foundations of Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning are reshaping every industry, yet getting started can feel overwhelming with mathematical complexity. This course simplifies these powerful technologies, giving you a clear, conceptual understanding of how computers learn from data.\n\nYou will transition from a curious beginner to a knowledgeable practitioner who understands how to frame problems, prepare data, and select the right models for real-world applications. By reading through practical explanations and step-by-step code walkthroughs, you will gain the confidence to discuss, design, and implement basic AI workflows.\n\nWhat you'll learn:\n- Understand the fundamental terminology of AI, supervised learning, and unsupervised learning\n- Explore modern dataframe libraries to clean, preprocess, and analyze raw datasets\n- Implement core machine learning algorithms including regression, classification, and clustering\n- Learn the basics of neural networks and how deep learning models process information\n- Discover modern generative AI concepts, including prompt engineering and retrieval-augmented generation (RAG)\n- Apply basic MLOps principles to evaluate, track, and maintain model performance over time\n\nWe begin with essential terminology and foundational concepts before moving step-by-step into data preparation, classical algorithms, and modern generative AI frameworks. Each concept is reinforced with clear written explanations and structured code snippets that you can read and analyze at your own pace.\n\nThis course is designed specifically for beginners, software developers looking to transition into AI, and analytical minds with no prior machine learning experience. Start your journey into the world of intelligent systems today.