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Data Science & Machine Learning Fundamentals

Machine learning is easy to buy and hard to judge. A vendor demonstration shows a model with 94 percent accuracy and the room nods, when the honest questions are what the base rate was, which errors the model makes, and whether the data it was trained on resembles the data it will meet. This day builds the understanding needed to ask those questions, and to work out which of your own problems are genuinely machine learning problems rather than reporting problems in disguise.

Programme Agenda

What Machine Learning Is Doing

Learning a pattern from examples instead of following written rules. Where that is the right approach and where a rule, a lookup table or a better report would do the job faster and more reliably.

Supervised, Unsupervised and the Rest

Classification and regression, clustering and anomaly detection, and where recommendation and forecasting sit. Matching a business question to a problem type.

The Data Requirement

How much data, of what quality, with what labels. Class imbalance, leakage from a column that would not exist at prediction time, and the historical bias a model will happily learn and reproduce.

Training, Validation and Testing

Why a model must be judged on data it has not seen, train and test splits, cross validation, and the time-based split that time series problems require.

Overfitting and Underfitting

The central failure mode, shown rather than described. Why a model that scores perfectly in development can be worthless in production, and the signals that give it away early.

Metrics That Match the Decision

Why accuracy misleads on imbalanced problems. Precision, recall, the trade between them, and choosing a threshold based on what a false positive and a false negative actually cost your business.

Interpretability and Trust

Feature importance, simple models against complex ones, and the regulatory and practical situations where you must be able to explain a decision to the person it affected.

Use Case Selection

Participants assess their own candidate use cases against data availability, decision value and error tolerance, and separate the promising ones from the ones that need a report instead.

Learning Outcomes:
Distinguish problems suited to machine learning from problems suited to rules
Match a business question to a problem type
Assess whether you have the data quantity, quality and labels required
Explain why models are evaluated on unseen data
Recognise overfitting and the signals that reveal it before deployment
Choose evaluation metrics based on the cost of each error type
Judge when interpretability is a requirement rather than a preference
Screen your own use cases for feasibility and value

Duration: 1 Day (8 Hours)
Training Hours: 9:00 AM to 5:00 PM
Level: Beginner
Training Mode: Physical, Online, or Hybrid
HRD Corp SBL-KHAS Claimable
Certificate of Completion included

Frequently Asked Questions

Analysts, managers and product owners evaluating machine learning proposals, IT staff supporting data science work, and anyone who needs to interrogate a vendor's model claims. Also a sound starting point for people intending to learn the technical side.

No. Models are demonstrated and their behaviour explored, but participants are not required to write code. Those wanting hands-on modelling should start with Python for Data.

Only to place them in context, because they solve a different class of problem. Applied generative AI is covered properly in the AI and Automation programmes.

Not in a day. The final module assesses your use cases for feasibility, which is usually the more valuable output at this stage and often prevents money being spent on a problem that a report would solve.

Yes, this programme is HRD Corp SBL-KHAS claimable. Our team can assist your HR department with the documentation required for the grant application.

Yes. Any programme can be booked as a team day. Everyone works the same brief together, so your people come away having built something and knowing each other better.

If you are claiming under HRD Corp, the session has to fall at least 14 days after your HRD Corp approval. If you are not claiming, the date is flexible and we work around your calendar.

Put them on the self-paced e-learning instead. Your team works through the modules on our LMS in their own time, sits the assessment, and earns the same certificate, so nobody has to clear a full day together.

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