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The goal D AI developer

Data: collect, clean, document

From observation to table, via strange values, licences and text preprocessing, to a documented dataset without leakage.

Knowledge nodes
40
From zero
about 24 h
Labs
6
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

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: dot product, norm and cosine similarityDin the browser · about 40 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: lists, loops and dictionariesCin the browser · about 40 minLab: mean, median and standard deviation by handCin the browser · about 40 minLab: the best line with least squaresCin the browser · about 40 minLab: your first Python functionsCin the browser · about 30 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer4 knowledge nodes
  1. Patterns and categories
  2. Collect data with a survey
  3. Sequences and precise instructions
  4. From observations to a table
BInvestigator8 knowledge nodes
  1. Data in everyday life
  2. Personal data: what do apps collect about you?
  3. Invalid values in the data
  4. Algorithmic thinking
  5. Rule-based systems and machine learning
  6. Source criticism and responsibility in AI use
  7. Training data, features and labels
  8. Classification: how a model sorts information
CBuilder9 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Python — strings and text processing
  6. Python — files, CSV and JSON
  7. Statistics — mean, median and spread
  8. Linear regression: fitting a straight line to data
  9. Sampling: who did we ask?
DAI developer15 knowledge nodes
  1. APIs and HTTP
  2. Regular expressions
  3. Licences and open data
  4. Text preprocessing
  5. Vectors
  6. Matrices and matrix multiplication
  7. Linear regression with several features
  8. NumPy — arrays and vectorisation
  9. Overfitting and generalisation
  10. Pandas — tables in Python
  11. Data quality: missing values, duplicates, errors
  12. Feature engineering
  13. Imbalanced classes
  14. Training, validation and test
  15. Data leakage
EUniversity4 knowledge nodes
  1. Personal data and anonymisation
  2. Annotation and labelling of data
  3. Dataset documentation and datasheets
  4. Web scraping — the technique and the rules