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Mai Studies 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

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Included with Mai Studies membership at no extra cost. Fee waivers are available through scholarships and open access.

AI & Applied Machine Learning course artwork

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 Mai Studies learning material. This is a professional completion programme, not a regulated qualification or university accreditation.

Programme syllabus

8 applied modules

Lessons: 16
Assessed tasks: 8
Practice questions: 96
Timed mock papers: 2

01

Machine learning systems and problem framing

Translate organisational questions into measurable learning tasks, baselines and success criteria.

2 lessons · 12 practice questions · 1 assessed task

1. From business question to learning task21 min study · 2 learning objectives · practical activity
Frame a model as one controlled part of a decision system.
2. Evidence, baselines and experimental discipline21 min study · 2 learning objectives · practical activity
Create a defensible experiment before model training.

Module 1 applied brief — marked out of 100, pass at 80%

02

Data preparation and reproducible pipelines

Profile, clean, transform and document data while preventing leakage.

2 lessons · 12 practice questions · 1 assessed task

1. Data quality, missingness and leakage20 min study · 2 learning objectives · practical activity
Prepare model-ready data without hiding quality risks.
2. Reproducible feature pipelines20 min study · 2 learning objectives · practical activity
Transform data consistently from training to operation.

Module 2 applied brief — marked out of 100, pass at 80%

03

Supervised learning

Build regression and classification models and compare them with meaningful baselines.

2 lessons · 12 practice questions · 1 assessed task

1. Regression, classification and regularisation20 min study · 2 learning objectives · practical activity
Select and train supervised models with appropriate constraints.
2. Probabilities, thresholds and decisions20 min study · 2 learning objectives · practical activity
Turn model output into controlled operational choices.

Module 3 applied brief — marked out of 100, pass at 80%

04

Evaluation and model selection

Use robust validation, threshold analysis and error investigation to select defensible models.

2 lessons · 12 practice questions · 1 assessed task

1. Validation and error analysis20 min study · 2 learning objectives · practical activity
Evaluate generalisation and locate systematic failure.
2. Fairness, robustness and model choice20 min study · 2 learning objectives · practical activity
Balance performance with risk and accountability.

Module 4 applied brief — marked out of 100, pass at 80%

05

Unsupervised learning and representation

Discover useful structure with clustering, dimensionality reduction and careful interpretation.

2 lessons · 12 practice questions · 1 assessed task

1. Clustering and dimensionality reduction20 min study · 2 learning objectives · practical activity
Discover structure without treating patterns as truth.
2. Anomaly detection and human review20 min study · 2 learning objectives · practical activity
Use unusual-pattern detection safely.

Module 5 applied brief — marked out of 100, pass at 80%

06

Neural networks and modern AI

Understand representation learning, neural architectures and responsible use of generative systems.

2 lessons · 12 practice questions · 1 assessed task

1. Neural networks and representation learning20 min study · 2 learning objectives · practical activity
Explain how layered models learn useful representations.
2. Generative AI systems and evaluation20 min study · 2 learning objectives · practical activity
Design grounded, testable generative workflows.

Module 6 applied brief — marked out of 100, pass at 80%

07

Responsible deployment and monitoring

Package models, manage versions, monitor drift and design human oversight.

2 lessons · 12 practice questions · 1 assessed task

1. Deployment contracts and monitoring20 min study · 2 learning objectives · practical activity
Move from notebook results to an operable service.
2. Human oversight and lifecycle governance20 min study · 2 learning objectives · practical activity
Operate machine learning with accountable controls.

Module 7 applied brief — marked out of 100, pass at 80%

08

Applied machine learning capstone

Deliver an end-to-end system proposal, model evidence and operational review.

2 lessons · 12 practice questions · 1 assessed task

1. Capstone: build the evidence pack20 min study · 2 learning objectives · practical activity
Integrate framing, data, modelling and governance evidence.
2. Capstone: operational review20 min study · 2 learning objectives · practical activity
Present a deployment recommendation to decision-makers.

Final applied project — marked out of 100, pass at 80%

Timed mock papers

AI & Applied Machine Learning: timed mock paper 1 — 180 minutes, 30 objective questions plus written and computational tasks
AI & Applied Machine Learning: timed mock paper 2 — 180 minutes, 30 objective questions plus written and computational tasks

Lesson pages, assessed tasks and mock papers open the moment you join this programme. Teaching material is studied online; the notes you write are yours to download.

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