Machine learning systems and problem framing
Translate organisational questions into measurable learning tasks, baselines and success criteria.
Free to enrol
Maicourses Original · AI & Machine Learning
AI & Applied Machine Learning
A nine-week professional programme moving from data and statistical foundations through supervised and unsupervised learning, neural networks, responsible AI, deployment and monitored decision systems. Learners complete practical labs, assessed briefs and an end-to-end applied project.
9 weeks 54 guided hours Intermediate
No course fee is charged for current published programmes.
What you will achieve
- Prepare reproducible datasets for machine learning
- Train and compare supervised and unsupervised models
- Evaluate performance, fairness and operational risk
- Deploy and monitor a documented prediction service
Entry guidance
- — Confident use of a computer
- — Basic algebra and spreadsheet skills
- — No prior machine-learning experience required
Original Maicourses learning material. This is a professional completion programme, not a regulated qualification or university accreditation.
Programme syllabus
Eight applied modules
6h
Data preparation and reproducible pipelines
Profile, clean, transform and document data while preventing leakage.
Supervised learning
Build regression and classification models and compare them with meaningful baselines.
Evaluation and model selection
Use robust validation, threshold analysis and error investigation to select defensible models.
Unsupervised learning and representation
Discover useful structure with clustering, dimensionality reduction and careful interpretation.
Neural networks and modern AI
Understand representation learning, neural architectures and responsible use of generative systems.
Responsible deployment and monitoring
Package models, manage versions, monitor drift and design human oversight.
Applied machine learning capstone
Deliver an end-to-end system proposal, model evidence and operational review.