Learning Path

Large Language Models Bootcamp

Here's what you'll learn:

Develop an understanding of how modern LLMs work and how they can be applied to real-world problems.

Design and implement end-to-end LLM applications using retrieval-augmented generation (RAG), vector databases, and effective prompt strategies to build reliable, context-aware systems.

Apply fine-tuning and evaluation techniques to optimize model performance

Enroll in this learning track and begin your journey to improving your skills.

29 Lessons

Introduction to Large Language Models

Explore large language models, their components, and challenges in enterprise. Learn embeddings, vector databases, and customization techniques for specific tasks.

42 Lessons

Transformer Architecture and Attention Mechanism

Master the foundations of Transformers and attention mechanisms to power AI applications. Understand tokenization, embeddings, and self-attention to enhance your AI skills.

20 Lessons

Fundamentals of Prompt Engineering

Gain essential skills in prompt engineering to design effective prompts for various tasks. Apply techniques to enhance in-context learning and tackle real-world applications confidently.

40 Lessons

A Practical Introduction to Vector Databases

Unlock the power of vector databases. Explore embeddings, optimization techniques, and advanced querying methods to build effective retrieval pipelines for today's applications.

38 Lessons

Mastering Langchain

Learn to build intelligent retrieval-augmented generation (RAG) systems that answer questions from your own documents. This course teaches you the essential components of RAG pipelines,from document loading and chunking to semantic search and context

35 Lessons

Evaluation of Large Language Models

Learn how to evaluate large language models for accuracy, safety, alignment, and performance using human and automated metrics to ensure reliable, ethical, and high-quality AI systems.

29 Lessons

Fine-Tuning Large Language Models

Learn the core principles of LLMs with a focus on fine-tuning, transfer learning, and methods like prompt tuning, prefix tuning, and LoRA. Gain hands-on practice customizing LLMs using a Llama2 7B quantized model.

22 Lessons

Challenges in Building RAG Applications

Master the practical skills to develop scalable, secure, and efficient LLM applications and RAG systems for business success.

19 Lessons

Final Project: Build A Multi-Agent LLM Application

Build and deploy an LLM app by choosing a project: a basic chatbot, a data-powered chatbot agent, or a chat-with-your-data app. Learn CI/CD, cloud deployment, and finish with a real project ready to scale in real scenarios.