Course catalogue
Three courses, each with a defined scope, stated prerequisites and a full syllabus
Python Foundations for ML · RAG Workshop · Applied Deep Learning Track. Full details on what each course covers, what it does not, and what you need to know first.
Back to HomeHow the courses are structured
Teaching methodology in plain terms
Prerequisites first, syllabus second
Every course page opens with a plain description of what you need to know before starting. A self-check quiz helps you decide whether the course is at the right level.
Workload stated as a segmented bar
Hours per week are shown as a static figure on the page — not as a vague range. You can plan your schedule before committing to a cohort.
"What this does not cover" is written out
Each course has a section listing topics that are deliberately out of scope. This is placed above pricing on the course page so you see it before you decide to enquire.
Course 01 · 8 weeks
Python Foundations for Machine Learning
Prerequisites
Comfortable with Python loops, functions and dictionaries. A short self-check quiz is available to help you decide honestly before enrolling. No machine learning background required.
An eight-week evening course covering the specific subset of Python that machine learning work actually uses. NumPy array thinking and vectorised operations, pandas for tabular data, matplotlib for visual inspection, and the habits — virtual environments, version control, notebooks that still run tomorrow — that keep later work from collapsing.
Taught live in small cohorts, recorded the same evening. Scope is deliberate: this course does not cover machine learning algorithms or model training. It covers the Python layer underneath those things.
What this course does not cover
- — Machine learning algorithms or model training
- — Deep learning or neural networks
- — Deployment or production infrastructure
- — Data engineering pipelines at scale
What is included
- Live sessions (2 evenings per week × 2 hours) + same-evening recordings
- Weekly exercise sets with written feedback from the instructor
- 3 portfolio-quality notebooks you keep outright
- Cohort discussion channel for the duration of the course
- Roughly 4 hours of homework weekly
RM 1,480
Enquire About This Course
Week-by-week structure
Environment setup and Python data model
Virtual environments, pip, version control basics, Python's object model as it applies to arrays.
NumPy: arrays, axes and broadcasting
Array creation, reshaping, broadcasting rules, indexing patterns used in ML code.
Vectorised operations and performance
Writing operations without Python loops, profiling, understanding where vectorisation matters.
pandas for tabular data
DataFrames, indexing, groupby, merging, common data cleaning patterns.
matplotlib and exploratory visualisation
Reading your data visually before modelling — distributions, correlations, anomalies.
Portfolio notebook review
Presenting and reviewing the three portfolio notebooks. Feedback on code quality and structure.
Workshop schedule (7 hours with breaks)
Chunking strategies
Why chunking decisions matter more than most people expect — fixed-size, semantic, and recursive approaches.
Embedding models
Choosing and comparing embedding models, understanding the trade-offs.
Vector stores and hybrid search
Setting up a vector store, running hybrid search, understanding recall-precision trade-offs.
Reranking and evaluation
Adding a reranker, measuring whether answers are actually grounded, honest evaluation approaches.
Build session
You build one complete working system on your own machine. Instructor available throughout.
Course 02 · 1 day
Single-Topic Workshop: Retrieval-Augmented Generation
Prerequisites
Comfortable with Python and the command line. No machine learning background required. You should be able to run a Python script, install packages, and navigate a terminal.
A one-day Saturday workshop, live online, on building a retrieval system around a language model. The scope is deliberately narrow: chunking strategies, embedding models, vector stores, hybrid search, reranking, and evaluation of whether answers are actually grounded. You build one working system during the day on your own machine and leave with the repository.
What this workshop does not cover
- — Model training or fine-tuning
- — Deployment at production scale
- — Prompt engineering as a general discipline
- — Agent frameworks or tool use
What is included
- 7 hours live workshop with breaks (Saturday, online)
- Maximum 20 participants
- Starter repository you keep
- Evaluation notebook
- Written reading list of primary sources
- Session recording
RM 620
Enquire About This WorkshopCourse 03 · 24 weeks
Applied Deep Learning Track
Prerequisites
Solid Python, comfortable with the command line, and either the Tensorloom Python Foundations course or equivalent hands-on experience. This track moves quickly — the Python layer is assumed, not taught.
The school's longest programme: a six-month, part-time track for engineers moving into deep learning work. Covers the mathematics you actually need rather than a full degree's worth, then works through training loops written from scratch, convolutional and transformer architectures, transfer learning, experiment tracking, data pipelines, distributed training basics, quantisation, serving, and monitoring in production.
Two-thirds of the time is spent on projects rather than lectures. Each learner ships four graded projects, each reviewed line by line by a practising engineer. The track closes with a capstone the learner defines themselves.
What this track does not cover
- — A full degree's worth of mathematics
- — Reinforcement learning
- — Multimodal models beyond vision + text
- — Hardware design or chip-level optimisation
What is included
- Live sessions twice weekly + recordings
- Four graded projects, each reviewed line by line by a working engineer
- Capstone project defined by the learner, with written review
- Mentor office hours each week
- Portfolio repository you own outright
- Roughly 12–15 hours per week commitment
RM 4,700
Enquire About This TrackInstalment arrangements may be available — mention it in your enquiry.
Track structure overview
Mathematics and training loops
Linear algebra and calculus as needed, autograd, writing training loops from scratch without a framework abstraction.
Architectures
CNNs, attention mechanisms, transformer architectures, transfer learning and fine-tuning strategies.
Training infrastructure
Experiment tracking, data pipelines, distributed training basics, mixed precision.
Production
Quantisation, model serving, monitoring in production, handling distribution shift.
Capstone
Learner-defined project, built over the final four weeks and reviewed with the same written feedback as the graded projects.
Which course is right for you
Choosing between the three programmes
| Feature | Python Foundations | RAG Workshop | Deep Learning Track |
|---|---|---|---|
| Duration | 8 weeks | 1 day | 24 weeks |
| Hours per week | ~8 hrs | 7 hrs (one day) | 12–15 hrs |
| Live sessions | |||
| Engineer-reviewed projects | exercise feedback only | build session | |
| Portfolio deliverables | 3 notebooks | 1 repository | 4 projects + capstone |
| Price (RM) | 1,480 | 620 | 4,700 |
| Best suited to | Engineers new to the ML Python stack | Engineers wanting hands-on RAG in one day | Engineers committing to deep learning work |
Across all courses
Standards that apply to every programme
Learner data privacy
Name, email and payment details held for operational purposes only. Not shared with third parties for marketing.
Syllabus currency
Syllabi are reviewed before each intake and updated when tooling or standard practice has changed enough to make existing content inaccurate.
Code quality in materials
Example code is typed, version-controlled, and written to run six months from the session date — not just during the course.
Enquiry response time
Enquiries are answered within one business day, Monday to Friday. We are willing to answer scope and prerequisite questions before you decide to enrol.
Cohort cap enforcement
Caps are not increased when demand exceeds them. When a cohort fills, a waitlist opens for the next scheduled intake.
No upsells during a cohort
The price stated at enrolment is the full price. There are no additional materials, upgrade tiers, or supplementary purchases offered once a course has started.
Pricing
Course fees in Malaysian Ringgit
All prices are full fees — no extras added during enrolment. Payment by bank transfer or debit/credit card.
Python Foundations
RM 1,480
8 weeks · per enrolment
- 16 live sessions
- 8 exercise sets with written feedback
- 3 portfolio notebooks
- Cohort channel access
RAG Workshop
RM 620
1 day · per session
- 7-hour live workshop
- Starter repository
- Evaluation notebook
- Session recording
Deep Learning Track
RM 4,700
24 weeks · per enrolment
- 4 reviewed projects + capstone
- Weekly mentor office hours
- Portfolio repository
- Instalment option available
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