Python AI Engineering: From Data Science to Generative AI

The demand for skilled professionals who can navigate both traditional data science and modern generative AI is growing rapidly. This written course provides a structured, step-by-step pathway to mastering these essential technologies using Python. You will start with the fundamental concepts of data analysis and statistics before moving into classical machine learning algorithms. From there, you will explore deep learning concepts and transition into modern AI engineering, including large language models, retrieval-augmented generation (RAG), and agentic workflows. By reading through clear explanations and analyzing practical code implementations, you will develop the skills needed to design and deploy intelligent systems. What you'll learn: - Understand foundational Python programming, modern virtual environments, and essential statistical concepts for data analysis. - Apply classical machine learning algorithms, including regression, classification, and clustering, to real-world datasets. - Build neural networks and deep learning models using TensorFlow to process complex data patterns. - Implement generative AI solutions using LLM APIs, prompt engineering, and modern retrieval-augmented generation (RAG) patterns with vector databases. - Design autonomous AI agents and workflows using advanced orchestration frameworks. The curriculum begins with core data science principles and statistical foundations, gradually progressing through classical algorithms and neural networks, and concluding with cutting-edge generative AI architectures. You will learn entirely through written explanations, code walkthroughs, and conceptual exercises designed to reinforce your understanding. This course is designed for beginners who want to enter the field of AI and data science, requiring no prior programming or machine learning experience. Start your journey into the world of intelligent systems and modern AI engineering today.