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Programmes & Curriculum

Three tracks.
One clear path forward.

Each programme at Neurogarden is built around a weekly rhythm of recorded lecture, live walkthrough, and practical exercise — designed to fit around a working schedule without losing the thread.

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How We Work

A weekly rhythm you can rely on

Every course at Neurogarden follows the same underlying structure, so participants know what to expect each week and can plan their study time without friction.

Recorded Lecture

Each week opens with a self-paced recorded lecture. Watch at any time — pause, rewind, or revisit as needed. No live attendance required for the core content.

Live Walkthrough

A scheduled live session where the instructor works through the week's key concepts in real time. Participants can follow along, ask questions, and see the reasoning behind each step.

Practice Notebook

Each week's practice notebook is a small, focused exercise. There is no grading — the notebook is for the participant's own benefit, to consolidate what was covered before the next session.

Final Project

Each programme concludes with a project of defined scope. The project is the participant's own work and belongs to them. Completion records are issued after the project and notebooks are submitted.

The Catalogue

Programme details

Three programmes, each with a clear scope and a defined outcome. The first two are ten-week courses; the third is a structured twelve-month programme for participants who want a longer arc of study.

Programme 01 · 10 weeks

Data Science Foundations

A ten-week online course covering the foundational layer of data science practice. The curriculum moves from Python for data analysis through pandas and basic statistical reasoning, then into an introduction to building simple predictive models. The pace is measured — each week builds on the one before, and the recorded lectures can be revisited as many times as needed.

The course is designed for working adults who already have some familiarity with Python. No advanced mathematics background is required, though participants who are comfortable with basic arithmetic and reading structured data will find the progression natural. The course concludes with a small, self-contained data analysis project that participants submit alongside their practice notebooks to receive a course completion record.

Python for data analysis — reading, filtering, and transforming structured data with pandas
Basic statistical concepts applied directly in code — distributions, aggregations, correlations
Introduction to building and evaluating simple classification and regression models
Weekly practice notebooks with a final data analysis project
Course completion record for participants who submit notebooks and the final project

Course fee

RM 540

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Programme 02 · 10 weeks

Practical NLP & LLM Track

A ten-week intermediate track on practical natural language processing and working with large language models. The track moves from the mechanics of tokenization and embedding models through prompt engineering patterns, retrieval-augmented architectures, and evaluation practices suited to language tasks in production settings.

Each week combines a recorded lecture, a live discussion session, and a small practical exercise. The track concludes with a small project built around an openly available model — the scope is defined by the participant and reviewed in the final session. Participants are expected to have either completed a foundations course or to have equivalent self-study behind them. How much participants take from the track depends on the work they put in.

Tokenization mechanics, vocabulary design, and the role of embedding models in NLP pipelines
Prompt engineering patterns — few-shot, chain-of-thought, structured output, and tool use
Retrieval-augmented generation architectures: chunking, indexing, and retrieval evaluation
Evaluation practices for language tasks — measuring what matters in real applications
Final project built around an openly available model, designed by the participant

Course fee

RM 1,520

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Programme 03 · 12 months

Yearlong AI Engineering Programme

A twelve-month programme for learners who want a structured year of study in applied AI engineering. The programme pairs cohort-based courses with self-paced reading, monthly mentorship calls, and a year-end project that the participant designs and owns entirely. It is not a modular collection of short courses — it is a single programme with a through-line.

Each participant is paired with a senior mentor for the full duration. The mentor relationship is not a weekly tutoring arrangement; it is a monthly call focused on the participant's progress, the year-end project direction, and the practical questions that arise from working through the material. The final project belongs to the participant — Neurogarden does not claim any stake in the work produced.

Paired courses covering the full arc from foundations through applied AI engineering practice
Monthly one-to-one mentorship calls with a senior practitioner for the full twelve months
Cohort-based live sessions and self-paced reading, structured to fit a working schedule
Year-end project of the participant's own design — fully owned by the participant
Programme completion record on successful submission of the year-end project

Programme fee

RM 2,820

Enquire About This Programme

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How to Choose

Programme comparison

Use this table as a starting point. If you are still unsure which programme fits your situation, write to us and we will help you think it through.

Feature	Data Science Foundations	NLP & LLM Track Intermediate	AI Engineering Yearlong
Duration	10 weeks	10 weeks	12 months
Price	RM 540	RM 1,520	RM 2,820
Format	Online, self-paced + live	Online, self-paced + live	Cohort + self-paced
Weekly live session
Practice notebooks
Final project
Monthly mentorship calls
Dedicated senior mentor
Completion record
Prerequisites	Basic Python familiarity	Foundations course or equivalent	Open to motivated learners
Best for	Starting out in data practice	Working with language models	A structured year of study

How We Work

What you can rely on

These are the operating principles that shape every programme we run — not aspirations written for a brochure, but the practical commitments we hold ourselves to each cohort.

Content stays current

The field moves. Lectures and notebooks are reviewed between cohorts to reflect the current state of the tools being taught — not a syllabus written two years ago and left to drift.

Practice over theory

Every concept is anchored to a working notebook. Participants leave each week with code they have run themselves, not slides they have scrolled through.

Small cohorts, real access

Cohorts are kept to a size where the instructor can hear individual questions in live sessions — not a broadcast with a chat box that nobody reads.

Your work stays yours

Projects and notebooks produced during a programme belong to the participant. Neurogarden does not claim rights to the work you build in our courses.

Recorded lectures, always available

Recordings are available to participants throughout the cohort period. Review a session before the live walkthrough, or catch up after a busy week — the recorded lecture does not expire mid-course.

Straight answers before enrolment

If you are unsure whether a programme fits your current level or schedule, write to us before enrolling. We would rather have that conversation early than have a participant spend ten weeks in the wrong course.

Pricing

Clear fees, no add-ons

Each fee covers the full programme — lectures, live sessions, practice notebooks, the final project, and the completion record. There are no module fees, platform charges, or extras billed at the end.

10 weeks