Machine Learning & AI Engineering Foundations

Build interpretable prediction experiments, challenge their evaluation and package a tested inference contract using inspectable Python examples.

What you'll be able to do

  • Define prediction-time features and evaluate with a time-aware split.
  • Compare regression baselines using losses that match the intended decision.
  • Interpret confusion matrices and select a threshold on validation data.
  • Explain voting and clustering without confusing them with guaranteed model quality.
  • Check a tiny neural model's gradient numerically.
  • Build a text-vector baseline and identify its limitations.
  • Version model metadata and enforce a tested inference contract.
  • Deliver a reproducible equipment-turnaround decision pack with documented limitations.

Before you start

  • Use Python functions, dictionaries, lists and basic file formats.
  • Understand SQL joins, filtering and aggregation.
  • Read basic algebra and run Python 3.12 or newer; no paid model API or cloud account is required.

The curriculum

  1. Freeze the Prediction Moment — Free preview

    Choose the target, feature availability and time boundaries before fitting a model.

  2. Choose a Loss Before a More Complex Model — Free preview

    Compare constant baselines to understand regression losses.

  3. Turn Scores into Review Decisions — Sign-in access

    Use confusion counts and a stated cost model to select a threshold without confusing scores with calibrated probabilities.

  4. Combine Votes and Discover Groups — Sign-in access

    Contrast supervised voting with unsupervised clustering using small examples whose limitations remain visible.

  5. Inspect a Neuron's Learning Step — Free preview

    Check one small gradient numerically before relying on an automatic differentiation framework.

  6. Represent Text with Inspectable Vectors — Sign-in access

    Build a small lexical similarity baseline and identify where word overlap stops representing meaning.

  7. Version and Test the Inference Contract — Sign-in access

    Package model metadata, feature expectations and rejection behavior before exposing predictions to another component.

  8. Deliver the Repair Forecast Decision Pack — Sign-in access

    Integrate the experiments into a reproducible equipment-planning project with clear human-review boundaries.

Repair Forecast

Create a local decision-support experiment for a fictional equipment repair desk. Estimate turnaround or flag cases likely to exceed an agreed planning window using only information available when a request opens. The output supports a coordinator's planning review; it does not diagnose faults, rank staff, allocate employment opportunities or make safety-critical maintenance decisions.

Deliverables

  • A synthetic equipment-request dataset with a feature-availability dictionary.
  • A chronological split manifest and excluded-record report.
  • Regression and classification baseline results with error analysis.
  • A versioned inference contract with rejected-input tests.
  • A model card, reproducible evaluation command and human-review decision pack.

Review your work

  • Features contain no closure-time facts or post-outcome technician notes.
  • Training, validation and test periods have distinct documented purposes.
  • Threshold selection never uses the final test labels.
  • Inference rejects missing, non-finite and unsupported values.
  • Reported results are labeled synthetic experiments, not production accuracy guarantees.