What do I need to know before starting?

The entry expectations are listed on this page. If you are close but not quite there, tell us at application; we run short pre-course preparation material and would rather get you ready than turn you away.

Does this course include the internship?

Yes, for learners who complete the core modules and meet the mid-point assessment bar. The internship is on live client work with a supervisor and code review, and you receive a written reference describing what you actually did.

Is there a certificate?

Yes, on completion of the capstone. More usefully, you finish with deployed projects, a public repository history, and a supervisor’s reference. Those are what interviews actually turn on.

What roles does Machine Learning prepare me for?

Typical destinations are Machine Learning Engineer, Data Scientist and ML Analyst. The curriculum is built backwards from what those interviews and first ninety days actually require.

Academy track68 hours · Beginner to Intermediate

Machine Learning

Name: Machine Learning
Availability: InStock

Algorithm-first learning with theory, coding, evaluation and Streamlit deployment

Apply for this trackPreview the syllabus

Fee on request

Duration: 68 guided hours
Level: Beginner to Intermediate

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Next cohort intake is open · instalments available

Why this track

Classical machine learning taught the way it is used at work: frame the problem, prepare the data, train and compare models, explain what they do, and publish a working demo. Every major algorithm is covered from intuition to code to evaluation, and the course ends in a deployed business prediction project.

68hours of guided, hands-on learning

8modules, each ending in something deployed

3portfolio projects, reviewed like client work

3+roles this track prepares you for

Outcomes

What you will be able to do

Frame regression, classification and clustering problems correctly
Prepare data using reproducible preprocessing pipelines
Train, tune, evaluate and interpret core ML algorithms
Publish a usable model demo through Streamlit and GitHub

Before you start

Entry expectations

If you are close but not quite there, tell us at application. We would rather get you ready than turn you away.

01Basic Python: functions, lists and dictionaries
02Elementary descriptive statistics

Tools you will use

Pythonpandasscikit-learnXGBoostLightGBMSHAPStreamlitGit & GitHub

Syllabus

A first look at the route

8 modules over 68 guided hours. Here is how it starts; the full roadmap opens up when you join.

M01ML problem framing and workflow
- Problem types, target/feature distinction, leakage and baselines
- Train/validation/test split and reproducibility
- Git/GitHub setup, repository structure and README
M02Data exploration and preparation
- EDA: shape, types, summary statistics, distributions and correlations
- Missing values, outliers and data quality checks
- Encoding, scaling, transformations and feature engineering
- ColumnTransformer and end-to-end preprocessing pipelines

6 more modules in the full roadmap

The complete syllabus, with every topic, hands-on lab and the capstone brief, is shared with learners when they join the course.

Join the course

Portfolio

What you will have built

01An end-to-end business prediction project (churn, pricing, risk or demand): EDA, pipeline, baseline, model comparison, evaluation and interpretation
02Streamlit demos for regression, classification and clustering models
03A documented GitHub repository for every project

Where it leads

Roles this prepares you for

Machine Learning EngineerData ScientistML Analyst

Questions

Before you apply

The course is 68 guided hours of explanation, guided coding and review, plus independent practice; admissions will share the cohort schedule. Live sessions are scheduled outside standard office hours and recorded, so working professionals can keep a full-time job while studying.