Machine Learning Fundamentals
capability building,
designed for your organisation.
A custom-built corporate programme for software engineers, data analysts, BI developers, junior data scientists, and technical professionals transitioning into ML/AI roles, plus product managers and analysts who need rigorous ML literacy. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Supervised vs. unsupervised vs. reinforcement learning — clean definitions and decision criteria
- The bias-variance trade-off, in plain English with worked examples
- Train/validation/test splits and why they matter
- Cross-validation patterns: k-fold, stratified, time-series, leave-one-out
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Demonstrable skills your team will apply on live projects.
Build, train, and deploy a production-grade ML model
Full lifecycle: ingestion, exploration, cleaning, feature engineering, training, evaluation, deployment, monitoring.
Apply the right algorithm to the right problem
Regression, classification, clustering, dimensionality reduction, ensemble, basic deep learning — chosen by problem fit.
Evaluate models with the discipline of a senior practitioner
Cross-validation, holdout, time-series CV, calibration, fairness audits — beyond simple accuracy.
Pass GSDC ML Foundations certification
Two attempts; cohort first-attempt pass rate 89%.
Ship a Kaggle-style capstone portfolio
Public evaluated capstone: notebook, code, write-up, evaluation — career-grade artefact.
Move into a junior/mid ML role
Equipped for ML engineer, data scientist, or applied ML transitions in IT services and product organisations.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Familiar with ML at conceptual level but cannot build, train, evaluate, and deploy a working model end-to-end
- ·Limited working knowledge of Python ML stack (scikit-learn, pandas, NumPy, PyTorch/TensorFlow)
- ·Cannot diagnose why a model is underperforming — overfitting? underfitting? data quality? feature engineering?
- ·No discipline for cross-validation, hyperparameter tuning, model selection, or evaluation beyond accuracy
- ·Unfamiliar with MLOps practices — model registry, monitoring, drift, retraining cadence
- ·Cannot critically evaluate ML proposals from data science teams or vendor pitches
Where they'll arrive
- ✓End-to-end ML practitioner — ingests data, engineers features, trains, evaluates, deploys, monitors a model
- ✓Algorithm fluency — applies regression, classification, clustering, dimensionality reduction, ensemble methods, and basic deep learning with appropriate judgement
- ✓Evaluation discipline — confusion matrix, precision, recall, F1, AUC, calibration, fairness — with the discipline to choose the right metric for the task
- ✓Production readiness — knows how to deploy, monitor, and retrain models
- ✓Kaggle-style portfolio — has a public, evaluated capstone with code, notebooks, evaluation, and write-up
- ✓Critical evaluation skills — can challenge ML proposals on technical merit
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
No, you do not need an advanced maths background to start learning Machine Learning. However, basic knowledge of algebra, statistics, probability, graphs, and logical thinking will help you understand how models learn from data. For beginners, the course can start with practical ML concepts first and gradually introduce the maths only where it is needed.