Skip to content

Applied Generative AI Engineering

Udacity

PaidintermediateSelf-pacedCertificate

Udacity's project-based nanodegree covers generative AI fundamentals, PEFT/LoRA and GRPO fine-tuning, RAG pipelines with ChromaDB, and multimodal apps with CLIP, Whisper, and diffusion models. For working developers who want mentor-reviewed projects and a career-oriented credential.

Who it's for

Developers and technically curious learners. This is an intermediate course in Generative AI & LLMs, running about ~56 hours (3 courses, 3 projects).

Cost at a glance

$249/month subscription. Udacity also lists a discounted 4-month bundle and a one-time individual purchase option; personalized discounts common. Seen 2026-11-01.

Compare the alternatives

Free alternative

Parlance Labs Educational Resources

Parlance Labs (Hamel Husain and collaborators) · Free

More in Generative AI & LLMs

Nir Diamant (DiamantAI)·GitHub
Free

Advanced RAG Techniques: Elevating Your Retrieval-Augmented Generation Systems

A large catalogue of runnable notebooks, each isolating one retrieval-augmented generation technique, from basic chunking and query transformations to reranking, hierarchical and graph-based retrieval, and evaluation. It suits developers who have built a basic RAG app and want to systematically try the techniques that improve retrieval quality.

intermediate 40+ runnable notebooks, self-paced
DeepLearning.AI and Amazon Web Services·Coursera
Free to audit

Generative AI with Large Language Models

Covers the full LLM lifecycle from transformer architecture and pre-training through instruction fine-tuning, RLHF, and deployment, taught by DeepLearning.AI and AWS engineers. The right course for Python developers with basic ML who want to understand how LLMs are actually built and adapted.

intermediate ~16 hours (3 weeks) Certificate
Free to audit · ~$49 Visit course
Hugging Face
Free

Hugging Face LLM Course

Hugging Face's hands-on course on LLMs and NLP using the Transformers, Datasets, Tokenizers, and Accelerate libraries, from fundamentals through fine-tuning, demos, and reasoning models. Best for Python developers who want to work with open models directly rather than only via APIs.

intermediate 12 chapters, self-paced