Path to Becoming
Become an AI Engineer
[ Learn the programming, data and evaluation behind useful AI applications. ]
Learning towards AI Engineer
Explore how models fit into working applications, from programming and data to retrieval and evaluation. These stages illustrate one direction; your own path depends on your background and goal.
Example practice
Example practice: use a small, permitted set of documents to answer a question, then inspect retrieval, errors and the limits of the result. Bring your reasoning and external project back for feedback.
An example path
1
Phase I: The Foundation
Start hereBuild the programming and mathematical foundations your project needs.
Linear Algebra & Calculus
Probability & Statistics
Python & Data Structures
Applied Programming
2
Phase II: Machine Learning
Build onCompare models and learn how to evaluate their behavior.
Supervised & Unsupervised Learning
Model Evaluation
Neural Networks
Feature Engineering
3
Phase III: Applied AI
PractiseConnect models to a useful task and examine reliability, cost and limitations.
Deep Learning Architectures
LLM & Generative AI Systems
MLOps & Deployment
Evaluation & Guardrails