The Catalogue
Three Courses.
One Clear Direction.
From a first practical machine learning course through to a full sixteen-week capstone programme — built in sequence for working engineers who want to move from studying AI to building it.
Back to HomepageMethodology
How the Courses Are Structured
All three Mindforge courses follow the same core format — a short recorded lecture to frame the week, a live coding session to work through the practical application, and a practice notebook to complete independently. The ratio of instruction to practice is deliberately tilted toward the latter.
Recorded Lecture
A short video covering the theory behind the week's topic. Watch it at whatever pace and time works for you. Kept brief so it supports rather than dominates the week.
Live Coding Session
The instructor works through the practical application in a live session. Participants follow along and ask questions. Sessions run outside standard office hours — evenings and selected weekend slots.
Practice Notebook
A structured notebook that participants complete and submit before the next week's session. Reviewed by a human instructor, with written feedback returned within a few days.
Course 01 · 10 Weeks
Hands-On Machine Learning
RM 510 · Suitable for working adults with a basic Python background
A ten-week practice-focused course combining short recorded lectures, live coding sessions, and small practice notebooks. Topics move through classical supervised learning, evaluation practices, and an introduction to neural networks. The course is framed around hands-on practice; participants build a working set of models across the ten weeks. A course completion record is provided on submission of all practice notebooks.
What you work through:
Course progression:
Weeks 1–3: Data handling, feature preparation, and first supervised models
Weeks 4–7: Evaluation frameworks, model selection, and handling real datasets
Weeks 8–10: Neural network basics, final model builds, notebook completion
Course 02 · 12 Weeks
Applied AI Engineering Block
RM 1,520 · Intermediate Python and prior model-training exposure expected
A twelve-week block focused on the engineering practices that surround working ML systems: data pipelines, training infrastructure, deployment patterns, and monitoring. The block combines live sessions with self-paced practice and a small applied project at the end. Outcomes depend on the participant's own work. The block is educational; the final project is the participant's own.
What you work through:
Block progression:
Weeks 1–4: Pipeline construction, data flow design, and environment setup
Weeks 5–8: Training infrastructure, tracking, and deployment fundamentals
Weeks 9–12: Monitoring, applied project build, submission and review
Course 03 · 16 Weeks
Forge Capstone Programme
RM 2,960 · For learners who have completed an intermediate block or hold equivalent experience
A sixteen-week capstone programme combining weekly cohort sessions, mentor pairing, and scheduled milestones leading to a final capstone project of the participant's own design. The capstone is reviewed by senior mentors and discussed in a closing cohort session at the end of sixteen weeks. The programme is educational; the capstone belongs to the participant.
What the programme includes:
Programme arc:
Weeks 1–4: Project scoping, mentor meeting, and first milestone
Weeks 5–10: Core build phase, mid-programme milestone review
Weeks 11–16: Finalisation, senior review, and closing cohort session
Which Course Fits?
Course Comparison
Use this to work out where to start, based on your current experience.
| ML Course | Engineering Block | Capstone | |
|---|---|---|---|
| Duration | 10 weeks | 12 weeks | 16 weeks |
| Fee (RM) | 510 | 1,520 | 2,960 |
| Python required | Basic | Intermediate | Intermediate+ |
| Prior ML experience | |||
| Live coding sessions | |||
| Mentor pairing | |||
| Applied project | |||
| Best for | Starting from scratch on ML | Engineers moving into MLOps | Building a substantial project with mentorship |
Shared Across All Courses
Operating Standards
Data Privacy
Participant data is collected only for enrolment and course delivery. No marketing sharing, no third-party data brokers. Governed by Malaysian law and our Privacy Policy.
Transparent Assessment
Completion criteria are stated before enrolment. Notebooks are assessed against clear rubrics. No subjective grading or hidden pass/fail thresholds.
Support During the Course
Questions that come up between sessions can be submitted to the team. Responses are returned within two business days. Technical support for platform access is handled on the same timescale.
Curriculum Versioning
Each cohort receives the current version of the course material. Outdated notebooks are removed after review. The field moves quickly; we revise accordingly after each cycle.
IP Clarity
Participants own their work — notebooks, projects, and capstone outputs — fully and without condition. Terms are stated in writing before enrolment, not buried in platform agreements.
Scheduling Commitments
Session dates for the full course run are communicated at enrolment. Schedule changes are communicated with a minimum of one week's notice. We follow the Malaysian public holiday calendar.
Fees
Course Pricing
All fees in Ringgit Malaysia. Instalment arrangements available for the Engineering Block and Capstone — ask when you enquire.
10 Weeks
ML Course
RM 510
- 10 recorded lectures
- 10 live coding sessions
- 10 practice notebooks
- Human review on each notebook
- Completion record
12 Weeks
Engineering Block
RM 1,520
- 12 recorded lectures
- 12 live sessions
- Practice notebooks and feedback
- Applied end-of-block project
- Project review by instructor
16 Weeks
Capstone
RM 2,960
- Weekly cohort sessions
- 1-to-1 mentor pairing
- 4 milestone reviews
- Senior mentor review of capstone
- Closing cohort presentation
Not Sure Which Course Fits?
Send an enquiry describing your current experience level and what you want to work toward. The team will suggest the right starting point and confirm when the next cohort opens.
Send an Enquiry