Our Courses
Three tracks, one coherent path from first principles to deployed AI
Each track is complete on its own. Together, they form a full progression — from understanding how AI learns to building and presenting original AI applications.
Back to HomeOur approach
How the tracks are structured and why
Neuralia does not treat its three tracks as separate products that happen to share a platform. They are designed as a progression: Foundations lays the conceptual and coding base; Neural Networks in Practice builds on it with deeper architecture work and real training cycles; Applied AI & Capstone assumes both of those and focuses on integration, responsibility, and presentation.
Learners who join at the intermediate or advanced level go through an enrolment conversation to confirm their background fits the entry point. This is not gatekeeping — it is to avoid placing someone in a track that assumes knowledge they do not yet have.
Each track uses real Python libraries, real datasets, and projects that can be documented and shared. The course structure is reviewed every six months. Cohort sizes are limited to keep mentor feedback meaningful.
Track 01
Foundations
Beginner · 6–8 weeks
฿3,400
Track 02
Neural Networks
Intermediate · 8–10 weeks
฿7,000
Track 03
Applied AI
Advanced · 10–14 weeks
฿11,900
AI Concepts & Foundations
A welcoming beginner track connecting the core ideas of modern AI with hands-on Python practice, so theory and doing reinforce each other. Gentle pacing for newcomers. Learners finish with a clear mental map and a few starter builds.
What this track covers
- How machine learning works conceptually — models, data, and training explained without assumed background
- Python fundamentals for data science: variables, loops, functions, NumPy, and Pandas
- Working with structured datasets: loading, cleaning, and exploring data
- First model builds using scikit-learn: regression, classification, evaluation
- Visualising results with Matplotlib; understanding what the numbers say
How the track runs
Weeks 1–2: Core AI concepts and Python setup; first exercises with simple data
Weeks 3–4: Data handling with Pandas; exploration and cleaning exercises
Weeks 5–6: First model builds; evaluation metrics; visualisation
Weeks 7–8: Final project with mentor feedback; documented submission
Neural Networks in Practice
An intermediate track on building and training neural networks with real data, taught through repeated, guided practice and honest evaluation. Mentor feedback throughout. Learners complete several end-to-end projects covering different architectures and problem types.
What this track covers
- Feed-forward networks: architecture, activation functions, gradient descent
- Training with PyTorch: data loaders, optimisers, training loops
- Convolutional networks for image classification; recurrent patterns for sequences
- Overfitting, regularisation, and honest evaluation on held-out data
- Three end-to-end projects with mentor review at each stage
How the track runs
Weeks 1–3: Neural network foundations; first PyTorch training loop
Weeks 4–6: CNN project with image data; evaluation and iteration
Weeks 7–8: Sequence modelling; recurrent architectures in practice
Weeks 9–10: Final project combining techniques; documented submission with mentor feedback
Applied AI & Capstone
A senior track on bringing models together into useful, responsible applications and a self-directed capstone. Emphasises sound engineering, careful evaluation, and clear documentation. Ends with a presented project for the portfolio.
What this track covers
- System design: combining models, APIs, and data pipelines into coherent applications
- HuggingFace Transformers: pre-trained models and fine-tuning for specific tasks
- Serving with FastAPI; experiment tracking with MLflow
- Evaluation fairness, bias assessment, and responsible documentation practices
- Self-directed capstone: proposal, build, documentation, and presentation to instructors
How the track runs
Weeks 1–3: System design patterns; integrating models with real APIs and data
Weeks 4–6: Transformers and fine-tuning; deployment with FastAPI
Weeks 7–8: Evaluation and ethics topics; capstone proposal with instructor feedback
Weeks 9–14: Capstone build, documentation, and final presentation session
Decision guide
Which track fits where you are now?
Use this to confirm your entry point — or contact us if you're unsure.
| Feature / Criteria | Track 01 Foundations |
Track 02 Neural Networks |
Track 03 Applied AI |
|---|---|---|---|
| Entry level | No prior experience | Foundations complete or equivalent | Neural Networks complete or equivalent |
| Python coding | Introduced from scratch | Applied throughout | Advanced patterns assumed |
| Mentor feedback | |||
| End project | Starter build | 3 end-to-end projects | Self-directed capstone |
| Duration | 6–8 weeks | 8–10 weeks | 10–14 weeks |
| Price (฿) | 3,400 | 7,000 | 11,900 |
Not sure where to start? Contact us — we can help you pick the right entry point.
Standards
What applies across all three tracks
Data privacy
Student data is used only for course delivery. Project data is kept on secure servers during the course period and handled under our Privacy Policy.
Quality review
All track content is reviewed every six months by the instructors who teach it. Student feedback from each cohort informs what changes for the next.
Responsive support
Questions receive responses within one working day (Mon–Fri, ICT). Enrolled students can schedule office hour sessions during active track periods.
Code documentation standards
All projects require written documentation. Students are marked on clarity and structure of documentation, not just whether the code runs.
Responsible AI principles
Topics like evaluation fairness, unintended bias, and clear limitation statements are embedded in the curriculum — most visibly in the Applied AI track.
Cohort size limits
Intakes are capped to preserve the quality of written mentor feedback. Once a cohort is full, the next intake is the earliest available starting date.
Pricing
Simple, fixed pricing in Thai Baht
One price per track, no subscription. You know the full cost before enrolling.
Track 01
AI Concepts & Foundations
฿3,400
one-time · 6–8 weeks
- All course material and exercises
- Mentor feedback on final project
- Support during track period
- Access to cohort discussion channel
Track 02
PopularNeural Networks in Practice
฿7,000
one-time · 8–10 weeks
- All course material and datasets
- Mentor feedback on all three projects
- Support and office hours
- Cohort discussion channel
Track 03
Applied AI & Capstone
฿11,900
one-time · 10–14 weeks
- All course material
- Capstone proposal review session
- Written feedback throughout
- Final project presentation to instructors
Questions?
Not sure which track to start with?
Get in touch and describe your background. We will confirm the right entry point and the next available cohort date.
Contact Us