Learning Path

Foundations of Generative AI for Business

Learn the theory and practice of building trustworthy AI applications.

You will learn:

Fundamentals of neural networks, generative AI, and transformer architecture

Building blocks of LLM applications: prompt engineering, embeddings, vector databases, context engineering, evaluation, and guardrails

Security, compliance, governance and responsible AI challenges

End-to-end lifecycle of building an AI product

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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.

40 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.

42 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.

27 Lessons

Introduction to Agentic AI

Explore the evolution of Agentic AI, its capabilities, and its impact on real-world AI systems. Gain insights into building smarter, more autonomous language models.

41 Lessons

Context Engineering

Elevate your automation skills by mastering the design of reliable, adaptive agent systems. Create workflows that respond to real-world needs and enable seamless agent collaboration.

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.

20 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.