Three Courses, One Coherent Path
From handling data for the first time to building and mentoring a deep learning capstone — every module is designed to prepare you for what follows.
← Back to HomeHow We Structure Learning
Each course follows the same rhythm: clear explanation of concepts, hands-on exercises with real data, written tutor feedback, and a graded project that asks you to put it all together.
Concept Introduction
Each topic is introduced with context — why it matters, what problem it solves, what you need to know first.
Guided Practice
Worked examples with annotated notebooks walk through the technique before you apply it yourself.
Exercise & Submission
You submit your work and receive written feedback from a tutor within three working days.
Module Project
A graded project brings the module's concepts together in a single task that tests understanding, not recall.
Data Foundations for AI
A gentle starting point covering the data skills that underpin AI work — handling, cleaning, and understanding datasets. Suited to those newer to the field who want a firm base before modelling. Includes weekly exercises and tutor support, paced over six weeks at a comfortable rhythm.
What you'll work on:
- Loading, inspecting, and cleaning real datasets in Python and pandas
- Understanding data types, distributions, and missing value patterns
- Exploratory analysis with visualisation using matplotlib and seaborn
- Basic feature engineering concepts that feed into modelling
How it runs:
Prerequisites: No prior coding experience required.
Machine Learning in Practice
A practical course on building and evaluating models, with an emphasis on understanding results rather than chasing them. Best for learners with basic Python and the data foundations covered. Includes graded projects and thoughtful code feedback, over ten weeks.
What you'll work on:
- Supervised learning methods: regression, classification, decision trees
- Model evaluation: cross-validation, confusion matrices, ROC curves
- Overfitting, underfitting, and how to diagnose them in practice
- scikit-learn pipelines and feature preprocessing
How it runs:
Prerequisites: Basic Python; Data Foundations or equivalent.
Deep Learning Projects
A project-focused track applying neural networks to real, manageable problems. Intended for those who have completed earlier modules or have similar experience. Includes a mentored capstone and honest, constructive review. Runs over twelve weeks at a steady weekly pace.
What you'll work on:
- Neural network fundamentals with PyTorch — layers, activations, backprop
- Convolutional networks for image classification tasks
- Sequence models and text processing with transformers
- Mentored capstone: a defined project with scheduled instructor check-ins
How it runs:
Prerequisites: Machine Learning in Practice or equivalent experience.
Which Course Is Right for You?
| Your situation | Course 1 RM 135 |
Course 2 RM 600 |
Course 3 RM 420 |
|---|---|---|---|
| New to data work and Python | ✓ | — | — |
| Know Python basics, want to build models | — | ✓ | — |
| Have ML experience, want deep learning | — | — | ✓ |
| Want to follow the full learning path | ✓ | ✓ | ✓ |
| Unsure where to start | Reach out — we'll discuss your background and suggest a starting point | ||
Not sure which row applies to you? Send us a message and we'll help you figure it out before committing to anything.
Standards We Apply to Every Module
Weekly Schedule
A defined weekly pace so you know what to expect, and when. No drifting, no sudden deadlines.
Written Feedback
Every submitted exercise receives a personal written response. The feedback explains your work, not just the correct answer.
Annotated Notebooks
All course materials include Jupyter notebooks with annotations explaining what the code does and why it's written that way.
Regional Datasets
Exercises use datasets from Malaysian and Southeast Asian sources where available, making examples feel grounded.
Annual Content Update
Library versions, framework choices, and code examples are reviewed and updated at least once per year.
Privacy by Default
Learner data is held only as long as needed. We don't share personal information with third parties for marketing purposes.
Straightforward Fees
One price per course, everything included. No add-ons for feedback, no premium tiers.
Data Foundations for AI
RM 135
Six weeks · Weekly exercises · Tutor feedback included
- Course materials & notebooks
- Written feedback on exercises
- Final module project review
- Completion document
Machine Learning in Practice
RM 600
Ten weeks · Graded projects · Code feedback
- Course materials & notebooks
- Written feedback on exercises
- Two graded project reviews
- Completion document
Deep Learning Projects
RM 420
Twelve weeks · Mentored capstone · Constructive review
- Course materials & notebooks
- Written feedback on exercises
- Mentored capstone with check-ins
- Completion document
Not Sure Where to Begin?
Send us a message with a bit about your background and what you're hoping to learn. We'll give you an honest view of where to start.
Get in Touch