↳ Kuala Lumpur · Part-time · Engineers only
Learn to build AI systems that actually run.
Three structured courses — ML foundations, deep learning, applied residency — taught by working engineers, not marketing copy.
Three courses. Stated prerequisites. No fluff.
Each course runs part-time so you keep your job while you study. Effort estimates are ranges, not marketing targets.
Foundations of Machine Learning Engineering
Prerequisites: can write a loop and read a stack trace
Python for numerical work, linear algebra, gradient descent from scratch, and the unglamorous work of loading and splitting a dataset. Written for software developers and analysts. Seven graded assignments, live sessions with a working engineer, and a Malaysian dataset project.
def gradient_descent(X, y, lr=0.01):
# from scratch, no sklearn
- Weekly live sessions
- Written feedback on all assignments
- Code review of your own model
- Malaysian dataset project included
Deep Learning Systems and Model Training
Prerequisites: foundations course or trains models at work
PyTorch dataloaders, mixed precision, distributed training across two or more GPUs, checkpointing, and debugging loss curves. Fourteen assignments, two open-ended projects with public write-ups, code review on every submission. GPU credits on a shared cluster included.
torch.distributed.init_process_group(
backend='nccl'
)
- GPU cluster credits included
- 14 assignments with code review
- Reproducibility requirement
- Public portfolio repository
Applied AI Engineering Residency
Prerequisites: production experience with deployed systems
Build and ship a working AI system end to end in a small cohort: data pipeline, evaluation harness, model, serving layer, monitoring, and a written post-mortem. Dedicated mentor, cluster access throughout, weekly design reviews, and a public technical talk at close.
pipeline → model → serving → monitor
+ eval harness + written post-mortem
- Dedicated mentor throughout
- Weekly design reviews
- Model risk & dataset provenance sessions
- Public technical talk at close
What makes this different from a tutorial
These courses are built around the things that a tutorial skips: graded assignments, code review, stated prerequisites, and honest effort estimates.
Taught by engineers
Each course is run by someone who trains models at work. Office hours are technical conversations, not sales calls.
Code review on submissions
Written feedback on every graded assignment. Not a rubric checkbox — a senior engineer reads your implementation.
Clear prerequisites
Every course states what it assumes before you enrol. There is a self-check quiz so you know if you are ready.
Honest effort estimates
Hour ranges, not single figures. "8 to 10 hours per week" tells you what you are signing up for. We do not round down.
Malaysian datasets
The foundations course includes a project built on Malaysian data. The context is local, not a recycled US benchmark.
Compute requirements stated
Each module tells you what hardware it assumes and what runs on a laptop. GPU credits are included where needed.
Not sure which course fits where you are now?
Send an enquiry and we will point you to the right starting point — or tell you plainly if none of them fit right now.
Frequently asked questions
Answers to what engineers usually ask before enrolling.
Do I need a degree in mathematics to take the foundations course?
No. The course teaches the linear algebra and probability that practising ML engineers use, not a university mathematics curriculum. The self-check quiz before enrolment will tell you if your existing background is sufficient.
Can I take the deep learning course without completing the foundations course first?
Yes, if you already train models at work. The prerequisite is the capability, not the specific course. Take the self-check quiz and read the first-module prerequisite statement before enrolling.
What does "part-time" mean in practice — how many hours per week?
The foundations course asks 8 to 10 hours per week. Deep learning asks 10 to 12 hours. The residency asks 15 to 20 hours. These are ranges based on what students actually report, not what fits on a marketing slide.
Do I need my own GPU or server?
The foundations course runs on a standard laptop. The deep learning course and residency include GPU credits on a shared cluster. Each module states its compute requirements in the first section.
Are the courses delivered in English?
Yes. All course materials, live sessions, and written feedback are in English. Technical terms follow standard engineering usage.
What is the refund or withdrawal policy?
Refund terms are set out in the Terms & Conditions. Please read them before enrolling. Send an enquiry if you have questions about a specific situation before you pay.
Does completing a course qualify me to work in a regulated field?
No. These courses teach engineering. Nothing in any Tensor Kopi course qualifies anyone to practise in a regulated field such as medicine, law, or financial advice. We say so plainly in the course materials.
How do I pay and in what currency?
All fees are in Malaysian Ringgit (RM). Payment options and accepted methods are confirmed at enrolment. Send an enquiry to begin the process.
Find Us
Level 11, Menara Binjai, Jalan Binjai, 50450 Kuala Lumpur
Send an Enquiry
We read every message. If none of the courses fit right now we will say so rather than enrol you anyway.
Contact Details
-
Phone
+60 3 2078 5194 -
Email
[email protected] -
Address
Level 11, Menara Binjai,
Jalan Binjai, 50450
Kuala Lumpur, Malaysia -
Working Hours
Monday – Friday: 9:00 – 18:00
Saturday: 10:00 – 14:00
Sunday: Closed