Become an AI Engineer
Build production applications on top of large language models.
11 lessons, in order
The route
Read these top to bottom. Nothing here assumes knowledge you have not been given earlier in the list.
-
Python for AI
Python basics
Python is a way of writing step-by-step instructions for a computer using words close to English. It is the language almost all AI is built in.
-
Machine Learning
What is machine learning?
Machine learning is how a computer works out a rule by looking at examples, instead of being handed the rule by a programmer.
-
Deep Learning
What is a neural network?
A neural network is a stack of tiny decision-makers that learns patterns from solved examples instead of following rules you wrote by hand.
-
Deep Learning
Transformers
A transformer reads every word at once and lets each word decide which other words matter to it, which is the architecture behind almost every modern…
-
Generative AI
What is a large language model?
A large language model is a program that guesses the next piece of text, over and over, until an answer appears.
-
Generative AI
Prompt engineering
Prompt engineering is writing your request so clearly that the model can give you something useful on the first try.
-
Natural Language Processing
Embeddings
An embedding is a list of numbers that stands for a word or a sentence, arranged so that things with similar meaning end up close together.
-
Generative AI
What is RAG?
RAG means the model searches your documents first and then answers using what it found, instead of answering from memory.
-
Generative AI
Vector databases
A vector database stores text as numbers that capture meaning, then finds the closest matches to your question in milliseconds.
-
Generative AI
AI agents
An AI agent is a language model placed in a loop where it can use tools, look at the result, and decide what to do next.
-
LLM Development
Model deployment
Deployment means putting your model somewhere with a public address that stays awake, so other people can use it when your laptop is shut.
When you finish
Build something with it. Reading alone will not make it stick — the first time you debug your own model is the moment it becomes real. Try the projects, and keep the error database open in another tab.