Three Courses, One Coherent Path Through AI
Each course is complete on its own, and each one prepares you well for the next. You can start where your background fits.
Back to HomeHow We Teach
Concept First
Each topic starts with why it matters and what problem it solves — before any code is introduced. Understanding the reasoning makes the implementation easier to retain.
Work Through It
Exercises are built around the week's material. You apply what you have learned on a real dataset or problem, not a simplified toy version. Your tutor reviews what you submit.
Consolidate and Move On
Feedback helps you identify what held and what did not before the next topic begins. We do not rush past gaps. Each week builds on the one before it.
Foundations of AI and Data
A calm grounding in the ideas and data skills behind AI, taught with clarity and patience. Suited to beginners and those moving from other fields. The course works through how data is collected, cleaned, and understood before a model ever sees it — because that is where most real-world work actually happens.
- 6 weeks of structured weekly lessons
- Exercises reviewed by your assigned tutor
- Covers Python basics, pandas, data visualisation, and statistical reasoning
- No prior technical background required
- Prepares you for Machine Learning Essentials
Machine Learning Essentials
A structured course on building and understanding models, with a focus on sound reasoning over quick results. You will work through the main categories of supervised and unsupervised learning, understand how to evaluate model performance honestly, and build projects that reflect real data science practice.
- 10 weeks with guided projects and feedback
- Covers regression, classification, clustering, and model evaluation
- Uses scikit-learn and real-world datasets
- Requires basic Python and the Foundations course (or equivalent)
- Prepares you for Deep Learning and Language Models
Deep Learning and Language Models
A focused track on neural networks and modern language models, applied to small, realistic problems. The course covers how neural networks learn, what attention mechanisms do, and how transformer-based models work — without overstating what they are capable of. A mentored capstone project runs across the final weeks of the course.
- 12 weeks including a mentored capstone
- Covers neural networks, transformers, and applied NLP
- Uses PyTorch and pre-trained models
- Capstone receives a written review from your mentor
- Machine Learning Essentials is a prerequisite
Which Course Fits Where You Are
| This fits you if… | Foundations RM 140 |
ML Essentials RM 590 |
Deep Learning RM 420 |
|---|---|---|---|
| No prior coding or data experience | |||
| Comfortable with Python basics | Optional | ||
| Understanding of data and statistics | — | — | |
| Completed ML Essentials (or equivalent) | — | — | |
| Want to understand language models | — | — | |
| Want a mentored project with written review |
Not sure which applies to you? Send us a message — we will give you a frank recommendation.
What Every Course Includes
Written Lesson Materials
Every topic is covered in clear written form — no requirement to watch video at a fixed pace. You can read and re-read at your own speed.
Cloud Notebook Exercises
Exercises run in cloud-based notebooks — no local setup required. You focus on the learning, not on environment configuration.
Tutor-Reviewed Submissions
Your submitted exercises are read by a human tutor who provides specific written feedback within one working day.
Data Privacy Standards
All datasets used in exercises are properly licensed or synthetic. We teach data handling practices that meet current professional expectations.
Material Reviewed Each Intake
We update course content before each cohort cycle to reflect current library versions and current practice in the field.
Direct Question Channel
You can ask your tutor questions directly throughout the course. Responses are written and specific, not automated suggestions.
Clear Pricing, No Extras
Foundations of AI and Data
RM 140
- 6 weeks
- All lesson materials
- Weekly exercises with feedback
- Tutor access
- Cloud notebook environment
Machine Learning Essentials
RM 590
- 10 weeks
- All lesson materials
- Guided projects with feedback
- Tutor access
- Cloud notebook environment
Deep Learning and Language Models
RM 420
- 12 weeks incl. capstone
- All lesson materials
- Mentored capstone project
- Written capstone review
- Tutor access throughout
All prices in Malaysian Ringgit. Employers may enquire about HRDC claimability.
Questions Before You Decide?
Ask about prerequisites, timelines, or what a typical week looks like. We will give you a clear, specific answer.
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