Professional skills

Machine Learning A-Z

Master machine learning algorithms

44 hours Intermediate Data Science & AI

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THE BIG PICTURE

About this course

Comprehensive machine learning course covering supervised and unsupervised learning, regression, classification, clustering, neural networks, and more. Implement algorithms in Python and R.

What you’ll learn

Explain problem framing and data preparation in a practical work context.
Make informed choices about supervised learning methods.
Recognize common pitfalls in unsupervised learning methods.
Connect validation, metrics and hyperparameters to a practical action plan.
Build confidence in model interpretation and responsible use.

A CLEAR PATH FORWARD

Recommended learning plan

6-day learning plan

Learn through a practical data or AI use case, with focused discussions, practical exercises and work you can take away.

Learning route: 6 recommended days
Practical work: 6 guided activities
Finish with: A practical review

Take a look at each day. Open a section to see the topics and practical work.

Day 1Problem framing and data preparation

What we’ll cover

Problem framing
Data preparation

Put it into practice

Work through a sample dataset or model, challenge its assumptions and explain what the results do and do not show.

You’ll take away
An analysis with assumptions and quality checks

Day 2Supervised learning methods

What we’ll cover

Supervised learning methods

Put it into practice

Review a workplace case and draft a fair, evidence-based response with clear ownership and follow-up.

You’ll take away
A people-practice action plan

Day 3Unsupervised learning methods

What we’ll cover

Unsupervised learning methods

Put it into practice

Review a workplace case and draft a fair, evidence-based response with clear ownership and follow-up.

You’ll take away
A people-practice action plan

Day 4Validation, metrics and hyperparameters

What we’ll cover

Validation
Metrics
Hyperparameters

Put it into practice

Investigate a failing or underperforming example and select checks that distinguish symptoms from causes.

You’ll take away
An evidence-based diagnostic checklist

Day 5Model interpretation and responsible use

What we’ll cover

Model interpretation
Responsible use

Put it into practice

Review a realistic dilemma and identify what can be used, what needs permission and where human review is essential.

You’ll take away
A decision log with clear safeguards

Day 6Deployment considerations and a model case

What we’ll cover

Deployment considerations
A model case

Put it into practice

Map the workflow, implement or sketch an automation step and plan how to recover when it fails.

You’ll take away
A workflow and recovery checklist

Bring it all together

Present the result alongside your evaluation, limitations and next steps.

Prerequisites

Bring a laptop. Coding-focused courses use Python; business-focused courses work with examples and decision tools.

Confirmed batch starts

Dates confirmed by MindClick. These are start dates, not full course timetables. Session hours, duration, fees, instructors, language, seats and classroom venues are not yet specified.

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Confirmed by MindClick · MC-1555