Foundational AI Development Using Large Language Models

Many businesses are integrating AI, but understanding how to move from theory to practical implementation can be challenging for newcomers. This course provides a structured path to mastering the foundational skills required for modern AI development. By the end of this course, you will have a solid theoretical understanding of neural networks and the practical ability to leverage pre-trained Large Language Models (LLMs) and advanced techniques like Retrieval-Augmented Generation (RAG) to build contextually relevant applications. What you'll learn: * Understand the fundamental concepts of machine learning, neural networks, and deep learning architectures. * Apply practical data preparation and feature engineering techniques essential for evaluating AI models. * Master the principles of effective prompt engineering for controlling and optimizing Large Language Model (LLM) outputs. * Configure vector storage and implement Retrieval-Augmented Generation (RAG) patterns for grounding LLMs in external data sources. * Practice evaluating model performance and address basic ethical considerations like bias and fairness in AI systems. The course begins with foundational AI terminology and mathematics, progressing through data handling and model training basics. We then shift focus entirely to practical implementation using modern LLM frameworks, culminating in building a working RAG application. This course is designed for absolute beginners in AI, current developers, or IT professionals who want to transition into the field of Artificial Intelligence development. No prior machine learning experience is required. Start your journey into the world of AI development today and unlock new possibilities.