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The goal D AI developer

Classical machine learning in practice

Logistic regression, trees, kNN and clustering — with cross-validation, the right metrics and bias–variance as the frame. Often better than a neural network on tabular data.

Knowledge nodes
43
From zero
about 25 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: k-means from scratchDa sandbox · about 45 minLab: k-nearest neighbours from scratchDa sandbox · about 45 minLab: 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 min

The whole path

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

AExplorer6 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
  3. Integer arithmetic and order of operations
  4. Comparing and sorting values
  5. Training a machine with examples
  6. Accuracy: how good is the model?
BInvestigator7 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Coordinates: positions on a grid
  5. Nearest neighbour: classification based on similarity
  6. Training data, features and labels
  7. Classification: how a model sorts information
CBuilder11 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. Probability — the basics
  9. Grouping without an answer key: clustering by hand
  10. Overfitting: the difference between learning the rule and memorising
  11. Linear regression: fitting a straight line to data
DAI developer18 knowledge nodes
  1. Conditional probability
  2. Bayes' theorem
  3. Text preprocessing
  4. Naive Bayes
  5. Vectors
  6. Clustering: k-means and hierarchical
  7. k-nearest neighbours (kNN)
  8. Matrices and matrix multiplication
  9. Linear regression with several features
  10. Logistic regression and decision trees
  11. Decision trees
  12. NumPy — arrays and vectorisation
  13. Overfitting and generalisation
  14. Pandas — tables in Python
  15. Regression metrics: MAE, RMSE, R²
  16. scikit-learn — the workflow
  17. Training, validation and test
  18. Cross-validation
EUniversity1 knowledge nodes
  1. The bias–variance trade-off