LLM Apps: Prototyping, Model Evaluation, and Improvements

About this course

Unlock the full potential of Large Language Models (LLMs) by understanding prototyping, model evaluation, and benchmarking. This hands-on course takes you through every stage of LLM development—from building and selecting models to fine-tuning, testing, and benchmarking them with industry-standard tools. Whether you're an AI beginner or a professional looking to enhance your expertise, this course provides the skills needed to create high-performing AI applications.

What You'll Learn: Set Up Your AI Development Environment

Learn how to prepare a powerful AI workspace with Python, VS Code, NPM, and essential AI libraries, ensuring a seamless development experience.

Understand AI & Machine Learning Basics

Explore key concepts in AI, Machine Learning, and Deep Learning, including supervised vs. unsupervised learning, model training phases, and how LLMs process and generate responses.

Selecting the Right AI Model for Your Use Case

Discover how to choose the best pre-trained AI models for NLP, vision, and multi-modal applications. Learn when to use classification, clustering, and regression models and understand model complexity, speed, and accuracy trade-offs. Harness the Power of Retrieval-Augmented Generation (RAG)Enhance your AI applications with RAG, a technique that combines retrieval-based search with LLM responses for more accurate and context-aware AI outputs.

Leverage the Hugging Face AI CommunityTap into the Hugging Face ecosystem—explore model repositories, learn about tokenizers and transformers, and contribute to the open-source AI movement.

Fine-Tune Models for Maximum

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