Introduction to Machine Learning
Supervised and unsupervised learning end to end — train, tune, evaluate and explain models with scikit-learn.
🎯 Who it's for
You've done Python & Maths for ML (or have equivalent experience) and want to train your first real models.
✅ Prerequisites
- Python with pandas and NumPy
- Basic linear algebra and probability
- Comfort reading library documentation
- Gradient-descent intuition helps
Topics covered
- The end-to-end ML workflow
- Regression and classification
- Trees, random forests and gradient boosting
- Clustering and dimensionality reduction
- Model evaluation, validation and metrics
- Data leakage, bias and explainability
Expected completion timeline
8 weeks part-time at 8–12 hours per week ≈ 1.8 months. Self-paced learners can go faster; the live cohort keeps this pace.
Supervised learning
You produce: A tuned classifier with a proper train / validation / test setup.
Ensembles and unsupervised methods
You produce: A clustering or segmentation analysis on real data.
Evaluation, explainability and project
You produce: A documented model with an evaluation report — the certificate project.
🧭 Where this fits
Core of the AI / ML Engineer path. See the career & salary map for the full path, the expected pay by region, and the total cost.
💳 Delivery & fees
Available in all four delivery formats. Fees vary by format and are confirmed on your advisor call; instalments available.
Add this to your plan
Book a call and we'll place this course in a full path toward the role you want.
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