Step 1
Ground the fundamentals
Andrew Ng’s specialization covers supervised/unsupervised learning, neural nets and the vocabulary you’ll use everywhere else.
Intermediate · Learning path
Build AI applications
Ship products on AI APIs, fine-tuning and RAG.
At a glance
- Who it’s for: Developers comfortable with Python who want to build and deploy AI-powered features and products.
- You’ll be able to: You will be able to design, build and deploy an AI application — from calling an API and grounding it with retrieval, through evaluation, to running it in production.
- Time: ~60 hrs · Steps: 5 · Code: Python
This path turns working knowledge of Python into the ability to ship. You’ll move from the fundamentals of machine learning into hands-on deep learning, then into the modern application stack: transformers, retrieval-augmented generation, vector databases and the operational concerns that decide whether an AI feature survives contact with real users.
The path
5 steps · all resources free to start
Step 2
Get hands-on fast
fast.ai’s top-down, code-first deep learning course gets you building models early.
Step 3
Learn transformers
The Hugging Face course teaches the library and the patterns behind nearly every modern NLP app.
Step 4
Build the app stack
Compose LLMs with tools, memory and retrieval over a vector database.
Step 5
Take it to production
Databricks’ course covers fine-tuning, RAG, evaluation, deployment and monitoring end to end.
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