The goal D AI developer
The developer's toolbox
NumPy, the terminal, regular expressions, modules and virtual environments, notebooks, debugging, code style and typing — the tools you use every day in an AI project.
- Knowledge nodes
- 32
- From zero
- about 21 h
- Labs
- 3
See what you already know — no account
The diagnostic removes what you already know, so your path is usually much shorter.
What you can do afterwards
- DJupyter and notebooks
- DTime complexity and big-O notation
- DData structures: lists, stacks, queues, hash tables
- DCode style, docstrings and the README
- DReading and understanding other people's code
- DPython — modules, packages and virtual environments
- DGit — version control
- DRegular expressions
- DRecursion
- DSorting and searching
- DThe terminal and the shell
- DDebugging and the debugger
- EType annotations and code quality
- DNumPy — arrays and vectorisation
- DPandas — tables in Python
- DSQL — the basics
- DVisualisation with Matplotlib
Labs along the way
You write the code. Tests you cannot see decide whether it holds up.
The whole path
Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map
AExplorer2 knowledge nodes
BInvestigator3 knowledge nodes
CBuilder6 knowledge nodes
DAI developer20 knowledge nodes
- Jupyter and notebooks
- Time complexity and big-O notation
- Data structures: lists, stacks, queues, hash tables
- Python — functions, scope and exceptions
- Code style, docstrings and the README
- Reading and understanding other people's code
- Python — classes and objects
- Python — modules, packages and virtual environments
- Git — version control
- Regular expressions
- Recursion
- Sorting and searching
- The terminal and the shell
- Testing with pytest
- Debugging and the debugger
- Vectors
- NumPy — arrays and vectorisation
- Pandas — tables in Python
- SQL — the basics
- Visualisation with Matplotlib