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Module 6 of 10

Module 6. Machine Learning Fundamentals

Executive Learning · Free learning

Machine Learning Fundamentals

How models are trained, evaluated and deployed, without the mathematics.

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Supervised, unsupervised, reinforcement

Supervised learning maps inputs to known labels, and it is where almost all commercial value sits. Unsupervised learning finds structure without labels, such as customer segments. Reinforcement learning optimises a sequence of actions against a reward.

Training, validation and overfitting

A model is trained on one slice of data and tested on another it has never seen. A model that performs brilliantly in training and poorly in testing has memorised rather than learned. That is overfitting, and it is the single most common failure in deployed models.

Evaluation that matters commercially

Accuracy is misleading on imbalanced problems. For fraud, precision and recall matter. For forecasting, error magnitude matters. Always ask what a false positive and a false negative cost the business.

Real-world case studies

Case study · Risk management

An insurer catches model drift before it reaches the loss ratio

  1. Situation

    An insurer deployed a claims fraud model that performed strongly for its first two quarters in production.

  2. Challenge

    Fraud tactics shifted, and the pattern the model had learned slowly stopped matching reality. Accuracy on paper stayed high because the historical benchmark had not been refreshed.

  3. How data was used

    Live claim outcomes were fed back weekly and compared against the distribution of the original training set to measure drift explicitly.

  4. How AI was applied

    A monitoring layer tracked feature distributions and precision on recent confirmed cases, triggering retraining once thresholds were breached.

  5. Business outcome

    Degradation was detected within weeks rather than after an annual review, and retraining restored performance before losses accumulated.

Lesson for the learner

Deployment is the start of the model lifecycle, not the end. Without monitoring, a good model quietly becomes a bad one.

Machine Learning Fundamentals, executive briefing

19:50 · Video briefing

Key takeaways

  • Supervised learning drives most commercial value today
  • Overfitting means the model memorised the training data instead of learning
  • Choose the evaluation metric by the business cost of each error type

Cost out your error types

Make the metric a commercial decision.

  1. Choose one predictive use case from Module 1
  2. Estimate the cost of a false positive and a false negative
  3. State which error the model should be tuned to avoid

Module 6 quiz

2 questions · 70% to pass

  1. 01A model scores 99 percent in training and 61 percent on unseen data. This indicates:

  2. 02For fraud detection where fraud is 0.2 percent of transactions, why is accuracy a poor metric?Applied

Answer every question to submit.

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