Learning Paths / AI Engineer Track
AI Engineer Track
A career track for AI engineers: Python, ML and LLM foundations, prompt engineering, embeddings, RAG, agents, and evaluation.
Start learning →Python for AI
- 1
Functions
Define reusable blocks of code with parameters, returns, and defaults
- 2
Classes
Define objects with attributes, methods, inheritance, and dunder methods
- 3
Async/Await
Write concurrent I/O-bound code with coroutines, the event loop, and asyncio
- 4
Type Hints
Annotate code with static types for tooling, clarity, and safer refactors
Foundations
- 1
AI Fundamentals
Core concepts, types of AI, and essential terminology for developers
- 2
Machine Learning Basics
Supervised and unsupervised learning, training, and inference for developers
- 3
Neural Networks
Layers, weights, forward passes, and activation functions explained
- 4
Large Language Models
Transformers, tokens, context windows, and what LLMs can and cannot do
Working with LLMs
Retrieval
- 1
Embeddings
Vector representations, similarity search, and practical use cases
- 2
Embeddings Deep Dive
Distance metrics, normalization, chunking, and dimensionality tradeoffs
- 3
Vector Databases
Storing and querying embeddings for similarity search at scale
- 4
RAG Basics
Retrieval-augmented generation pipeline for developer applications
- 5
Advanced RAG
Hybrid search, reranking, query rewriting, and grounded generation