Foundations of Full-Stack AI Engineering

Ready to move beyond AI theory and learn how to build complete, working applications? This course guides you through the entire process, from writing the first line of code to deploying a functional model. You will gain the practical skills to manage the full lifecycle of an AI project. You'll learn to process data, build and train machine learning and deep learning models, and apply modern MLOps practices to deploy and maintain your applications, building a solid foundation as a versatile AI practitioner. What you'll learn: - Master Python fundamentals for data science, including working with NumPy and Pandas for data manipulation. - Build classic machine learning models for regression and classification using Scikit-learn. - Understand the principles of neural networks and construct deep learning models with TensorFlow or PyTorch. - Explore the fundamentals of Generative AI and build basic applications with Large Language Models (LLMs) using LangChain. - Apply core MLOps principles by containerizing your applications with Docker and tracking experiments. - Learn to serve your models as APIs using a web framework like FastAPI. The course starts with core Python and data analysis concepts, then progresses through machine learning, deep learning, and finally to the principles of deploying and managing AI systems. This course is designed for absolute beginners. No prior experience in programming or artificial intelligence is required to get started. Start your journey into building end-to-end AI applications today.