Three courses. Each with a defined scope and clear expectations.
From mathematical groundwork to applied Python practice to research literacy — courses organised so you know exactly what you are taking on before you begin.
Back to HomeHow each course is structured
Each Cendekia course follows the same structural principles: a defined scope stated upfront, weekly sessions building on each other in a logical sequence, project-based assessments with individual written feedback, and access to recorded sessions so the pace of a working life does not put you behind.
Before opening each new cohort, we review course content against how the field has moved. The three courses are designed to complement each other — you can take the Foundations course and the Applied Python course in sequence, or take either independently, depending on your background.
All sessions recorded
Available throughout the cohort period and after completion.
Written feedback
Every assessment reviewed individually by the course lead.
Weekly office hours
Live sessions with advance question option for those who cannot attend.
Tangible deliverables
Project work you can keep and reference after the course ends.
Foundations of Machine Learning
This course covers the mathematical and conceptual groundwork of machine learning — the layer that sits beneath the frameworks and makes their behaviour intelligible. Topics include linear algebra refreshers, probability essentials, gradient-based optimisation, and the structure of supervised and unsupervised learning tasks. It is suited to working professionals with some programming experience who would like a solid base before moving into more applied work.
What you will work through
- Linear algebra and probability — focused on what matters for ML
- Gradient descent and the mechanics of model training
- Supervised learning: regression, classification, evaluation
- Unsupervised learning: clustering, dimensionality reduction
- Project-based assessments reviewed with individual written feedback
How the twelve weeks are arranged
Mathematical refreshers — linear algebra and probability in the context of learning systems
Optimisation and the training process — gradient descent, loss functions, regularisation
Supervised learning in depth — models, evaluation methods, practical considerations
Unsupervised methods and final project — pulling the concepts together in applied work
Applied Python for AI Development
An applied course on the Python practices that appear in real AI workloads — project structure, data handling with pandas and numpy, working with notebooks and scripts together, and a careful introduction to PyTorch. The course assumes some prior Python familiarity and focuses on the patterns that developers actually reach for in practice, rather than on comprehensive language coverage.
What you will work through
- Project structure and environment management for AI work
- NumPy and pandas for data loading, cleaning, and transformation
- Using notebooks alongside scripts — when each is appropriate
- PyTorch: tensors, autograd, and building a first model
- Weekly deliverables that build on each other across the eight weeks
How the eight weeks are arranged
Environment setup and project structure — working practices for AI development
NumPy and pandas — data handling at the scale and shape AI work requires
Notebooks and scripts together — organising work across different formats
PyTorch introduction and final project — tensors, autograd, a working model
Reading AI Research Papers
A short course for learners who want a calm, supported introduction to reading contemporary AI research. Each session focuses on one paper, walking through its structure, claims, methods, and limitations together. Participants leave with a practical reading method they can apply on their own afterward — so the course continues to be useful long after the two weeks are done.
What the two weeks cover
- How AI research papers are structured and why
- Reading claims, methods, and limitations critically
- Two complete papers read session by session with the group
- A portable reading framework to keep and use independently
How the two weeks are arranged
First paper — reading structure, understanding claims, examining the methodology together
Second paper — applying the reading method, evaluating limitations, consolidating the approach
Which course fits your situation?
A comparison of what each course covers, to help you self-select.
| Feature | ML Foundations | Applied Python | Research Reading |
|---|---|---|---|
| Duration | 12 weeks | 8 weeks | 2 weeks |
| Fee (MYR) | RM 2,015 | RM 1,150 | RM 580 |
| Good if you have some programming background | |||
| Covers mathematical concepts | |||
| Hands-on Python coding | |||
| Introduces PyTorch | |||
| Teaches how to read research papers | |||
| Lowest commitment to try Cendekia |
ML Foundations
Professionals who want to understand why ML models behave as they do, not only how to use them.
Applied Python
Developers who want to work with AI tooling more fluently and understand the patterns underneath the libraries.
Research Reading
Anyone who wants to follow the primary literature rather than relying on summaries, with a low time commitment.
What applies across all courses
Personal data kept private
Enrolment information is used only for course administration and direct communication. Not shared with third parties for marketing. PDPA 2010 compliant.
Post-cohort quality review
Every cohort ends with a feedback collection. Responses inform both the next content review and how courses are described to future learners.
Content updated before each cohort
AI moves quickly. We review course material before each new cohort opens and update where the field has shifted in ways that matter for learners.
Limited cohort size
We keep cohort numbers manageable so that individual written feedback and responsive office hours are genuinely feasible, not aspirational.
Pre-enrolment conversations welcome
If you are uncertain whether a course suits your background or goals, contact us before enrolling. We are glad to talk it through.
All-inclusive fixed pricing
One fee covers all materials, recordings, office hours, and individual assessment feedback. No separate charges or premium tiers within each course.
Course fees
All prices in Malaysian Ringgit. Each fee is all-inclusive.
ML Foundations
per enrolment
- All session recordings
- Course materials
- Weekly office hour access
- Individual assessment feedback
- Completion record
Applied Python
per enrolment
- All session recordings
- Course materials and code samples
- Weekly office hour access
- Individual assessment feedback
- Completion record
Research Reading
per enrolment
- Session recordings
- Reading framework document
- Two papers read together with the group
- Lowest commitment to try Cendekia
Have a question before you commit to enrolment?
We are glad to talk through prerequisites, scheduling, or anything else that is on your mind. Send us a message or call during office hours.
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