Three ways to study AI
at your own level.
From a gentle introduction with no prior background required, through applied Python tooling, to a six-month project track with live sessions and individual feedback.
Back to HomeHow we structure the learning
step one
Readings and context
Each topic begins with written material that builds the conceptual background. We don't jump straight into exercises before the framing is in place.
step two
Exercises and project work
Exercises apply what was covered in the readings. In the Python and Project Track programmes, these are notebook-based and produce working code the participant keeps.
step three
Feedback and review
Submitted work receives written comments from an instructor. Where live sessions are included, they serve as a space to revisit material that's caused confusion.
Introduction to AI Concepts
A short online course that introduces the core ideas behind modern AI systems through readings, small exercises, and discussion threads. The pace is gentle and assumes no prior coding background. Participants finish with a clear mental map of what AI systems do and a portfolio of written reflections on the topics covered.
What the course covers
- What machine learning is and how it differs from rule-based software
- Common AI applications and how they work at a conceptual level
- How AI systems are trained and what that process involves
- Limitations and failure modes worth understanding
- Written reflection portfolio of 5–7 short entries
Programme structure
Duration: 3–5 weeks, self-paced
Format: Written readings, exercises, discussion threads
Support: Email response within 1 working day
Outcome: Written reflection portfolio
Applied Python for AI
An eight-week online programme covering the Python tooling commonly used in AI projects. Topics include notebook workflows, data handling with pandas, basic model libraries including scikit-learn, and a small end-of-course project that ties the material together. Sessions combine written tutorials, recorded walkthroughs, and weekly live office hours.
What the programme covers
- Python basics and Jupyter notebook workflows
- Data handling and preparation with pandas
- Exploratory data analysis and visualisation
- Basic model training with scikit-learn
- End-of-course project: a working ML notebook
Programme structure
Duration: 8 weeks
Format: Written tutorials, recorded walkthroughs, exercises
Support: Weekly live office hours (MYT) + email
Outcome: Working ML notebook and end-of-course project
Full Project Track
A six-month structured track guiding participants through a portfolio of small applied AI projects — from data preparation through to deployment basics. Includes weekly live sessions, written feedback on all submitted work, and a final project review. The track is educational in nature and does not represent a formal qualification.
What the track covers
- Data sourcing, cleaning, and preparation workflows
- Model selection, training, and evaluation
- Introduction to deployment and serving basics
- Portfolio of 4–6 completed applied projects
- Final project review with written instructor feedback
Programme structure
Duration: 6 months
Format: Written material, weekly live sessions (MYT), project work
Support: Written feedback on all submissions + weekly sessions
Outcome: Portfolio of 4–6 applied AI projects
Choosing the right programme
A feature comparison across the three programmes to help you decide where to start.
| Feature | Intro (RM 480) | Python (RM 1,650) | Project Track (RM 4,600) |
|---|---|---|---|
| No coding background needed | Basic comfort helpful | Python required | |
| Self-paced format | Scheduled sessions | ||
| Weekly live office hours | |||
| Written feedback on submissions | |||
| Working code as output | |||
| Portfolio of applied projects | 1 end-of-course | 4–6 projects | |
| Best for | Curious beginners | People who want to write AI code | Committed learners building a portfolio |
Standards we apply across all programmes
Data security
Participant data and submitted work are stored on secured infrastructure. Access is restricted to the Neurova team. Full details in our Privacy Policy.
Annual curriculum review
All three programmes go through a structured annual review. Technical references and exercises are updated when the field has moved in meaningful ways.
Responsive support
Email enquiries receive a personal response within one working day. Office hours are held in MYT. Recordings are available for participants who cannot attend live.
Honest programme descriptions
What's on the Solutions page is what participants get. We don't describe a programme as covering something it doesn't, and we're clear about prerequisites.
Accessible delivery
Course materials are screen-reader compatible and work on lower-bandwidth connections. No proprietary software or hardware is required to participate in any programme.
Cohort feedback
Structured feedback is collected at the end of every cohort. Responses are reviewed by the curriculum team and used to inform the next iteration of each programme.
Programme fees (Malaysian Ringgit)
All fees are listed in RM and include access to all course materials and support for the duration of the programme. No add-on costs.
Introductory
Introduction to AI Concepts
RM 480
one-time fee · self-paced access
- 3–5 weeks self-paced
- Written readings and exercises
- Written reflection portfolio
- Email support
Applied
Applied Python for AI
RM 1,650
one-time fee · 8-week programme
- 8-week structured programme
- Tutorials and recorded walkthroughs
- Weekly live office hours (MYT)
- Written feedback on submissions
- End-of-course ML project
Extended
Full Project Track
RM 4,600
one-time fee · 6-month programme
- 6-month structured track
- Weekly live sessions (MYT)
- Written feedback on all work
- 4–6 applied project portfolio
- Final project review session
Send us a message and we'll help you decide
Describe your background and what you're hoping to learn. We'll suggest the right starting point — no pressure to enrol immediately.
Get in Touch