Three courses. Each one takes you further into the stack.
From Python foundations through neural network training to shipping production AI systems. Every course is a cohort with live sessions, code review and a project you finish.
How the courses are structured
Cohort format
Fixed group, shared pace
Individual feedback
Written notes on your code
Project deliverable
Something you built and can show
Python for Data and Modelling
A starter cohort for people who can write a little code and want the specific Python skills that modelling work depends on. Topics cover data structures and vectorised thinking, NumPy and pandas in depth, reading messy real data, plotting for understanding rather than presentation, writing testable analysis code, and version control habits that survive a team. Learners finish with a cleaned dataset, an analysis notebook and a short written finding. Open to analysts, engineers and researchers changing direction.
What is included
- Ten live sessions with replays
- Weekly exercises with written feedback
- Code review on the final notebook
- Cohort forum access throughout
- One mentor call
- Completion record
Process overview
- 01Data structures, vectorised thinking and NumPy
- 02pandas in depth — wrangling real messy data
- 03Exploratory plotting and understanding outputs
- 04Writing testable analysis code and version control
- 05Final notebook project with code review
Neural Networks and Model Training
An eighteen-week cohort taking learners from fundamentals to training their own networks with judgement about what is going wrong and why. Coverage includes gradient descent and its variants, initialisation, normalisation, regularisation, convolutional and recurrent architectures, attention, transfer learning, and reading loss curves as diagnostic information. A significant portion is spent on debugging training runs, which is where most practical time actually goes. Suited to those who have completed a fundamentals course or equivalent self-study.
What is included
- Eighteen live sessions
- Cloud compute credits
- Weekly code review
- Three mentor calls
- Training-run debugging clinic
- Final project with written assessment
- Completion record
Process overview
- 01Gradient descent, initialisation and normalisation
- 02CNNs, RNNs and attention mechanisms
- 03Transfer learning and fine-tuning
- 04Loss curve reading and debugging clinic
- 05Final project — trained model with assessment
Applied AI Systems Programme
A thirty-week programme on building systems around models rather than models alone. Modules cover retrieval pipelines, embedding stores, evaluation harnesses, prompt and context management, latency and cost engineering, guardrails and failure handling, observability, and the documentation and consent questions that arise when a system is put in front of users. Learners ship three working systems and one substantial capstone reviewed by practitioners working in industry. Aimed at engineers who will be responsible for something running in production.
What is included
- Thirty weeks of live teaching
- Compute credits throughout
- Weekly code review
- Assigned mentor for the full duration
- Three shipped-project assessments
- Architecture review clinic
- Practitioner-reviewed capstone
- Detailed completion record
Process overview
- 01Retrieval pipelines and embedding store design
- 02Evaluation harnesses and context management
- 03Latency, cost and guardrail engineering
- 04Observability and failure handling in production
- 05Capstone: full system reviewed by industry practitioners
Choosing the right course
If you are unsure which course fits, this matrix helps. Contact us and we will give you a specific recommendation.
| Feature | Python Course RM 480 · 10 wks |
Neural Networks RM 1,640 · 18 wks |
Applied Systems RM 4,280 · 30 wks |
|---|---|---|---|
| Prerequisite coding level | Basic (any language) | Python + fundamentals | Engineer-level Python |
| Cloud compute credits | |||
| Mentor calls | 1 | 3 | Assigned mentor, full duration |
| Debugging clinic | |||
| Practitioner-reviewed capstone | |||
| Best for | Analysts & career-changers | ML engineers at foundations stage | Engineers building production systems |
Not sure which row you are in? Send us a message — we will help you decide.
Shared standards across all three courses
Data privacy
Exercise submissions and project code are not shared outside the cohort. Learner data is used only for course administration.
Curriculum refresh each cohort
Material is reviewed and updated between runs. A change log is published before each cohort starts.
Session recording policy
All live sessions are recorded and available to cohort members for the duration of the course.
HRD Corp eligible
All three courses qualify for HRD Corp claimable training, allowing Malaysian employers to claim training levy reimbursement.
Forum support throughout
The cohort forum is monitored by instructors during the week. Questions get answers that engage with the actual code or problem shared.
Completion records that show the work
Every completion record describes what was built and the assessment criteria applied — not just course name and date.
Pricing overview
[ 10 weeks ]
Python for Data and Modelling
- 10 live sessions + replays
- Weekly exercise feedback
- Final notebook review
- 1 mentor call
- Cohort forum
[ 18 weeks ]
Neural Networks and Model Training
- 18 live sessions + replays
- Cloud compute credits
- Weekly code review
- 3 mentor calls
- Debugging clinic
- Final project assessment
[ 30 weeks ]
Applied AI Systems Programme
- 30 weeks live teaching
- Compute credits throughout
- Assigned mentor, full duration
- 3 shipped-project assessments
- Architecture review clinic
- Practitioner-reviewed capstone
Prices shown are per-cohort enrolment fees. HRD Corp claimable. Instalment options available — ask when you contact us.
Tell us your background and we will point you in the right direction
Not sure which course makes sense for where you are? Send a brief note — what you do now, what you have already learned, what you want to build — and we will give you a direct answer.
Get in Touch
Each course runs as a cohort — a fixed group of learners who start and progress together. The cohort format means questions in the forum come from people at the same stage as you, live sessions can address what the current week's material is actually producing, and code review feedback is calibrated to where the group is, not a generic rubric.
The curriculum is organised into weekly units. Each unit has a notebook to work through, exercises to submit, and a slot in the live session for discussion. Replays are posted within 24 hours for learners who cannot attend live. Exercises are returned with written notes within five working days.
All three courses end with a project that has been assessed by the instructor. The Python course ends with an analysis notebook, the neural networks course with a trained model and write-up, and the applied systems programme with a capstone reviewed by practitioners.