Three Tracks. One Clear Direction.
Start where you are and work forward — each track designed to bring you a genuine step further in understanding AI and machine learning.
Back to HomeBuilt Around How Understanding Actually Develops
Each of our tracks follows the same core principle: you don't get a new concept until you have what you need to make sense of it. This means shorter sections, more frequent project work, and feedback that keeps you connected to why each thing matters.
Our methodology isn't based on moving through a syllabus quickly. It's based on moving through it in a way that leaves you able to think independently by the end.
Concepts before complexity
We teach the idea before the formula. You always know what something is trying to do before seeing how it works.
Review then advance
After each section, short review exercises check understanding before the next layer opens.
Projects at every stage
You apply each concept to actual data — not toy examples — so the learning is anchored in practice.
Feedback loops built in
Mentor reviews and community discussion are part of the process, not optional extras.
Intro to Data & Models
A gentle starter course introducing data fundamentals, basic statistics, and how simple models learn from examples. Built for absolute beginners, it uses plain language, visual explanations, and short practical tasks. A learning experience focused on understanding, not on promises about what comes after.
- No coding or math background required
- Visual explanations with annotated worked examples
- Short practical tasks after every section
- Community access included throughout
How you work through it:
- 1Data fundamentals and types
- 2Basic statistics and distributions
- 3How models learn from examples
- 4Mini-project: build and evaluate your first model
Building with Neural Networks
A hands-on track guiding learners through building, training, and improving neural networks on real datasets, one project at a time. Includes structured assignments, mentor code reviews, and a community space to ask questions and share progress.
- Project-based assignments on real datasets
- Mentor code review on every submission
- Work with PyTorch, pandas, and scikit-learn
- Community space for questions and sharing
Project sequence:
- 1Understand the fundamentals of a neural network
- 2Build and train your first network on tabular data
- 3Improve it — tune, debug, and compare
- 4Apply the same process to image or text data
Deep Learning Mentorship Program
An in-depth program with regular mentor sessions supporting learners as they design and complete an advanced deep-learning project at their own pace. Includes feedback, study resources, and community access. A guided, supportive learning experience.
- Regular scheduled one-on-one mentor sessions
- You define your own project and direction
- Curated study resources for deep learning
- Community access and written feedback on all work
Program structure:
- 1Scoping session: define your project with your mentor
- 2Structured learning phase with resource pack
- 3Build and iterate with regular mentor sessions
- 4Final review and documentation of your project
Which Track Is Right for You?
Not sure where to start? This comparison should help you make sense of what each track involves.
| Feature | Track 1 Starter |
Track 2 Neural Networks |
Track 3 Mentorship |
|---|---|---|---|
| Prior knowledge needed | None | Track 1 or equivalent | Track 2 or equivalent |
| Mentor code reviews | |||
| Live video sessions | |||
| Own project definition | |||
| Community access | |||
| Price (฿) | 4,200 | 15,500 | 31,000 |
Not sure which to pick?
If you've never worked with data before, Track 1 is the place to begin. If you've covered the basics and want to build something real, Track 2 is likely right. If you're ready to take on an independent project with regular mentor support, Track 3 is designed for that. Reach out and we'll help you decide.
How We Maintain Quality Across All Tracks
Privacy & Data Security
Learner data is stored securely and used only to deliver your course access and mentor communication. No advertising use, no data sharing.
Quarterly Curriculum Review
Content is reviewed every quarter against current practice in the field. Libraries, examples, and reading lists are updated when better options appear.
Mentor Quality Standards
All mentors at Neuronest work in or have worked in applied machine learning. Their feedback reflects real professional standards, not textbook expectations.
Academic Integrity
Project submissions should represent your own work. Our review process is designed to encourage genuine learning — mentors ask questions to understand your thinking, not just evaluate output.
Moderated Community
Community spaces are actively managed so they remain useful. Mentors participate regularly, and there's a clear process for flagging issues or concerns.
Satisfaction Tracking
We collect feedback after every track completion and read every response. Recurring suggestions go into the next curriculum update.
Simple, Transparent Pricing
One-time payment in Thai Baht. No subscriptions. No hidden fees.
Intro to Data & Models
- Full course access
- Practical tasks and mini-project
- Community access
- Mentor code reviews
Building with Neural Networks
- Full track access
- Real dataset projects
- Mentor code reviews
- Community access
Deep Learning Mentorship
- Your own advanced project
- Regular live mentor sessions
- Written feedback on all work
- Study resources + community
Not Sure Which Track to Start?
Send us a brief note about your background and what you're hoping to work on — we'll help you figure out the right place to begin.
Get in Touch