Python & Maths for Machine Learning
The working toolkit behind every model: NumPy, pandas, and the linear algebra, calculus and probability you actually use in machine learning — taught through code.
🎯 Who it's for
Anyone comfortable with basic programming who wants a solid, code-first foundation before starting an ML track.
✅ Prerequisites
- Comfortable writing basic code in any language
- School-level algebra; willingness to pick up the rest
- Use of the terminal and a code editor (we set these up)
- No prior machine-learning experience needed
Topics covered
- Python for data — NumPy, pandas, plotting
- Linear algebra that matters for ML
- Calculus, gradients and optimisation
- Probability and statistics
- Vectorised thinking and performance
- From maths to code: gradient descent from scratch
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.
Python and the data stack
You produce: A cleaned dataset and an exploratory analysis notebook.
Linear algebra and calculus, in code
You produce: Gradient descent implemented from scratch on a real dataset.
Probability, statistics and a mini-project
You produce: A documented analysis with uncertainty quantified — the certificate lab.
🧭 Where this fits
First step for the AI / ML Engineer, Data Engineer and CV / NLP Engineer paths. 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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