Three Tracks. One Clear Path Through AI Development.
Each Neuronova track is built around a focused set of skills, delivered in a sequence that makes technical sense. Pick where you are and work through to where you want to be.
Back to HomeHow Neuronova Courses Are Built
Every track is designed around four principles: technical sequence, working examples, honest difficulty framing and responsive instructor support.
Sequential Design
Modules are ordered by dependency. You encounter each concept at the point where you have the context to understand it.
Applied Exercises
Each lesson includes code you can run, modify and break deliberately. Understanding comes from doing, not from watching.
Realistic Pacing
Time estimates per module are accurate. We'd rather you know something takes ten hours than be surprised when it does.
Instructor Access
Students can submit questions to instructors at any point in the course. Responses arrive within one working day.
AI Development Fundamentals
A structured course covering Python foundations, data handling and the building blocks of machine learning models. Suited to motivated beginners building a steady base over several weeks.
- Python syntax, data types, functions and file handling
- NumPy and pandas for numerical and tabular data work
- Supervised learning concepts and scikit-learn workflows
- Model evaluation, overfitting and cross-validation
- Final project: building and evaluating a complete ML pipeline
How This Track Unfolds
Python environment setup and core language concepts — variables, control flow, functions and working with files.
Data handling with NumPy and pandas — loading, inspecting, cleaning and transforming datasets.
Introduction to machine learning — model types, training, evaluation and scikit-learn implementation.
Capstone project — apply the full pipeline to a realistic dataset, document findings and submit for instructor review.
฿3,500
Duration: 6–8 weeks at ~8 hrs/week
Deep Learning Specialization
A focused track on neural network design, training techniques and model tuning with applied exercises. For students who have completed the fundamentals and want depth.
- Neural network architectures — feedforward, recurrent, attention
- Training dynamics — loss landscapes, optimisers, learning rate schedules
- Regularisation techniques — dropout, weight decay, batch normalisation
- PyTorch implementation from tensors to training loops
- Debugging and diagnosing poorly trained models
How This Track Unfolds
PyTorch fundamentals — tensors, autograd, and building a basic training loop from scratch.
Network architecture design — layer types, depth, width and the decisions that shape model capacity.
Training stability — diagnosing gradient issues, selecting optimisers and tuning learning rate schedules.
Applied project — design, train, evaluate and document a deep learning model on a realistic task.
฿7,700
Duration: 8–10 weeks at ~8–10 hrs/week
Computer Vision Intensive
A project sprint covering image data, model training and evaluation for visual tasks. Practical and outcome-focused across a defined timeframe.
- Image data pipelines — loading, augmentation and batch preparation
- Convolutional neural network design and implementation
- Transfer learning using pre-trained backbones
- Object detection and image classification evaluation
- End-to-end sprint project with instructor review
How This Track Unfolds
Image data fundamentals — OpenCV basics, torchvision datasets, data loaders and augmentation pipelines.
CNN architecture — building convolutional layers, understanding receptive fields and spatial feature maps.
Transfer learning and fine-tuning — adapting pre-trained models such as ResNet for new visual tasks.
Sprint project — complete a full computer vision pipeline from raw images to trained, evaluated model.
฿12,250
Duration: 4–6 weeks at ~12 hrs/week
Which Track Is Right for You?
Use this table to match your current background and goals with the appropriate starting point.
| Fundamentals ฿3,500 |
Deep Learning ฿7,700 |
CV Intensive ฿12,250 |
|
|---|---|---|---|
| No prior coding experience needed | ✓ | ✗ | ✗ |
| Requires Python background | ✗ | ✓ | ✓ |
| Covers neural networks in depth | ✗ | ✓ | ✓ |
| Focused on image and visual data | ✗ | ✗ | ✓ |
| Self-paced with access after enrolment | ✓ | ✓ | ✓ |
| Best for: beginners | ✓ | ✗ | ✗ |
| Best for: developers deepening ML knowledge | ✗ | ✓ | ✗ |
| Best for: engineers targeting vision roles | ✗ | ✗ | ✓ |
What Every Track Includes
These standards apply across all three Neuronova tracks without exception.
Data Privacy
Student data is managed in line with Thailand's PDPA. No data is shared with third parties for marketing purposes.
Verified Code
All starter code and exercise files are run and tested before each module goes live. Students work with code that functions as documented.
Instructor Support
Questions reach instructors directly. One working day response time is the standard. Support is available in English and Thai.
Curriculum Updates
Modules are reviewed every six months. Library updates and methodological shifts are reflected in course material on a regular cycle.
Peer Community
All enrolled students have access to the community platform. Sharing progress and code review between peers is encouraged.
Extended Access
Access to course materials continues after the initial enrolment period. No recurring fees are required to revisit completed modules.
Course Fees in Thai Baht
One-time fees with no subscriptions. Access extends beyond the study period.
AI Fundamentals
฿3,500
- 6–8 weeks at ~8 hrs/week
- Python, pandas, scikit-learn
- Capstone project included
- Instructor support
- Community access
Deep Learning
฿7,700
- 8–10 weeks at ~8–10 hrs/week
- PyTorch, neural networks
- Applied project included
- Instructor support
- Community access
CV Intensive
฿12,250
- 4–6 weeks at ~12 hrs/week
- OpenCV, CNNs, torchvision
- Sprint project included
- Instructor support
- Community access
Not Sure Where to Start?
Send us a message with a brief note about your current background and we'll suggest the most suitable entry point.
Send an Enquiry