A personal study route through ML and AI
One finite route
through ML and AI
Start with practical Python and data work, build a first machine-learning model, then deepen the mathematics, algorithms and systems behind modern AI. Every unit says what to open, how far to go, how to practise and when to stop.
- Core stages
- 7
- Sections
- 2
- Core units
- 22
Curriculum · 01—07
Choose your starting point
Seven connected stages, from practical foundations to the mechanics behind modern language models.
Python & Data Foundations
Learn enough Python and data tooling to inspect, clean and explain a real dataset.
Mathematics for Machine Learning
Build the linear algebra, calculus, probability and optimization intuition needed to understand how machine-learning models represent data and learn from it.
Algorithms & Data Structures
Reason about the data structures and algorithms behind efficient programs.
Classical Machine Learning
Build and evaluate reliable predictive models from tabular data.
Deep Learning with PyTorch
Understand neural networks and train them with PyTorch.
Systems Foundations
Understand the operating-system, networking and hardware concepts behind AI workloads.
LLM Foundations
Understand the architecture and execution of modern language models.