What you get here that's harder to find elsewhere
The difference isn't the subject matter — it's how the programmes are taught, how small the cohorts are, and how straightforward we are about what you're getting into.
Back to HomeSix things that shape the experience
These aren't marketing points — they're the actual decisions behind how the programmes are designed and delivered.
Live sessions, not pre-recorded lectures
Every programme includes regular live sessions where you can ask questions and work through problems in real time — not just watch and hope it clicks.
Portfolio work at every level
From the starter project in the Foundations Programme to the full capstone in the Advanced track, you finish with work worth showing.
Deliberately small cohorts
We cap cohort sizes so mentors can give meaningful feedback on your actual code and projects — not automated scores or generic comments.
Curriculum updated between cohorts
AI tools and libraries move fast. We review and update content after every intake, so you're working with current techniques, not archived ones.
Three programmes that build on each other
The tracks are designed to connect — Foundations leads to ML Engineering, which leads to Advanced AI. You know where each one takes you before you start.
Plain communication throughout
We don't inflate what a programme will do for your career. We describe what's covered, what the workload is, and what prior knowledge suits each level.
Mentors who've worked in the field
The people who teach at Synaptik have professional backgrounds in machine learning engineering, data science, and AI development. They've worked on real systems, encountered real problems, and bring that into how they frame exercises and answer questions.
- Industry experience in ML engineering and deployment
- Feedback grounded in real project experience
- Willing to say "this is the difficult part" honestly
Practitioners, not just educators
The team's day-to-day familiarity with AI tools and workflows shapes what gets taught and how feedback is given.
Current tools, real environments
Python, common ML libraries, version control, and deployment environments — the same stack practitioners use.
Working with tools the field actually uses
Programmes use the tools and libraries that practitioners reach for in their daily work — nothing invented for teaching purposes only. You'll write Python, handle real datasets, use version control, and by the Advanced track, work through model deployment in a realistic environment.
- Python and ML libraries used in current practice
- Real datasets across all tracks
- Deployment environments in the Advanced programme
A programme where questions get real answers
One of the most common frustrations with online learning is hitting a problem and having nowhere to turn. At Synaptik, live sessions are the space for that — mentors address specific questions about specific code, not generic guidance pulled from a script.
- Weekly live sessions with mentors
- Direct feedback on submitted work
- One-to-one mentoring in the Advanced track
Feedback that's specific to your work
Not automated. Not generic. Someone who has read your code and can explain what needs to change and why.
Transparent pricing, no hidden tiers
RM 620, RM 2,350, and RM 4,520 — the programme fees cover everything included and are stated clearly before you commit.
Pricing that reflects what's actually included
Each programme fee covers live sessions, learning materials, mentor feedback, and the project support included in that track. There are no add-on fees for things that should be part of the learning experience. Pricing is shared upfront so you can make an informed decision before enquiring.
- All materials and sessions included in the fee
- No surprise add-ons mid-programme
- Payment options discussed during enrolment
What you leave with
Every programme is designed around concrete outputs — a starter portfolio project in Foundations, applied portfolio pieces in ML Engineering, and a capstone in the Advanced track. These are tangible pieces of work participants finish and keep, not abstract learning points to carry in their heads.
- Portfolio project at every level
- Completion record for the Foundations Programme
- Capstone presentation in the Advanced track
Tangible outputs, not abstract claims
Work you can point to, share, and discuss — built during the programme, finished by the end of it.
How Synaptik sits next to other options
A factual look at what tends to be different about studying with us versus other AI learning options.
| Feature | Typical online courses | Synaptik |
|---|---|---|
| Live sessions with a mentor | ||
| Portfolio projects built during the programme | ||
| Personalised code feedback | ||
| Curriculum reviewed each cohort | ||
| Honest about what each programme covers | Varies widely | |
| Small cohort sizes | ||
| One-to-one mentoring available | (Advanced track) |
Distinctive features of the Synaptik approach
A structured path from starter to advanced
The three Synaptik tracks form a coherent path: Foundations, ML Engineering, Advanced AI. Each builds on the previous and the skills don't start over — they accumulate. This isn't common in AI education, where programmes often treat learners as starting fresh each time.
Based in Sarawak, accessible across Malaysia
Synaptik started in Kuching and has a physical presence on Jalan Tabuan — which means there's a real team behind the programmes, not just a platform. Delivery is online, so learners across Peninsular Malaysia and East Malaysia can participate with the same access.
Portfolio work built into the structure, not optional
Many programmes offer "capstone projects" as optional extras. At Synaptik, project work is the centre of the learning experience — exercises, project reviews, and portfolio pieces are built into every week of every programme, not offered as optional additions.
Designed to fit around existing commitments
The programme schedule is built with working adults in mind. Sessions are timetabled, workload is planned, and the pace assumes you're fitting this around a job or other responsibilities — not studying full-time. Eight to twelve hours a week is a realistic expectation for most participants.
Numbers that reflect the work
3
Structured programmes, built to connect
180+
Learners who've completed a programme
6+
Years of AI education experience across the team
4.7
Average participant satisfaction across cohorts
MSC Malaysia Technology Partner
Recognised under Malaysia's digital economy programme
SSM Registered Business
Formally registered with Suruhanjaya Syarikat Malaysia
Sarawak Digital Economy Association Member
Active member of regional tech community
Ready to have a proper conversation about which programme fits?
We're happy to talk through your background, what you're hoping to build on, and which track makes sense to start with. No pressure — just an honest conversation.
Get in Touch