Guided Learning Path
From intuition to working experiments
Use this path when you want a clear route through the site: understand the pattern, build a small model, evaluate it honestly, then test assumptions in a browser lab or sandbox.
Beginner
ML Foundations
~60 min · no prior ML required
Step 1
Know Your Data
Distribution shape, outliers, and why summaries can hide failure modes.
Step 2
How Models Learn
Gradient descent as a practical training loop: loss, updates, stopping, and failure.
Step 3
Classification Metrics
Precision, recall, false positives, and why accuracy is often the wrong target.
Step 4
Honest Evaluation
Cross-validation, leakage checks, and the difference between tuning and testing.
Build
First Real-Data Workflow
~75 min · includes local CSVs
Case 1
Housing Regression
Load a local sample, split the data, fit a baseline, and report MAE/RMSE.
Step 2
Bias vs Variance
Diagnose underfitting, overfitting, and whether more data will help.
Step 3
Regularization
Control model complexity without blindly tuning the test set.
Run
JupyterLite Lab
Open the real notebook in a Jupyter interface directly in the browser.
Intermediate
Time Series and Drift
~90 min · production-minded validation
Step 1
Stationarity
Check whether the structure of the series is changing over time.
Step 2
Walk-Forward Validation
Avoid random splits and evaluate models in time order.
Step 3
Data Drift
Monitor PSI/KS and connect distribution shift to model decay.
Case 2
Energy Forecast
Use local demand data to build lag features and compare against a naive baseline.
Advanced
Explainability and Market Evidence
~90 min · evidence-aware decisions
Step 1
SHAP Values
Explain predictions while avoiding causal overclaims.
Step 2
Market Evidence Legend
Separate statistical risk tools from heuristic chart and indicator hypotheses.
Step 3
Validate Indicators
Treat indicator signals as hypotheses and test costs, drawdown, and walk-forward stability.
Case 3
Market Backtest
Use the local OHLCV sample to test mechanics before making any claim.
Cases
Local Dataset Workflows
Four mini-cases with CSV samples, pipelines, metrics, pitfalls, and notebook snippets.
Sandbox
Build Mode
Save, compare, export JSON, copy Python, and reset interactive experiments.
Lab
JupyterLite
Run the notebook in the browser without a local Python install.