Full course description
This course, presented in a "Train the Trainer" format, provides a comprehensive exploration of Machine Learning (ML) and Deep Learning (DL), equipping participants with foundational knowledge and practical skills. It begins with an introduction to ML, DL, and Generative AI, distinguishing between their concepts and applications. Participants will learn essential mathematical tools, Python programming, and no-code visualization techniques to implement AI models effectively. The curriculum covers supervised and unsupervised learning methods, including clustering, decision trees, and recommendation systems, as well as reinforcement learning. Neural networks and advanced deep learning techniques, such as VAEs and applications of Generative AI, are thoroughly examined. Hands-on projects focus on supervised, unsupervised, and deep learning models, structured around the AI project cycle. The course concludes with an analysis of emerging trends and technological advancements shaping the future of ML and DL, preparing participants to innovate and excel in the AI-driven landscape.