Three tracks · English · Online
Our Courses, Explained
From a structured introduction to programming and AI, through to building and deploying machine learning systems — find the track that fits where you are today.
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The Neuronest Learning Method
Each Neuronest track follows a layered design: foundational concepts first, then applied practice, then review and iteration. You don't move to the next stage until the current one is solid — and mentor feedback is built in throughout, not just at the end.
Projects are central to every track. Rather than completing exercises that only make sense inside the course platform, learners build things they can share outside of it — code in a repository, models that run, systems that do something.
Structured sequence
Clear order through material
Active learning
Build, not just read
Mentor review
Feedback on your code
Iterate and improve
Refine based on notes
Foundations of AI Development
A structured entry course introducing programming for AI, core mathematics in plain terms, and the building blocks of machine learning. Learners work through guided lessons, small exercises, and a first hands-on project, with mentor feedback along the way. Suited to those newer to coding who want a steady, well-paced start.
- Python basics and numerical computing
- Core mathematics for ML explained clearly
- Building blocks of machine learning models
- Hands-on project with mentor feedback
- Community forum access throughout
How the track unfolds
Python environment setup and core programming concepts
Mathematics essentials — linear algebra and probability in plain language
Introduction to machine learning concepts and first models
Hands-on project build with mentor review and feedback
Applied Machine Learning Track
An intermediate track focused on building and evaluating models with real datasets, covering data preparation, common algorithms, and practical tooling. Learners complete several portfolio projects and receive structured code reviews. Best for those comfortable with basic programming who want applied practice. Runs across sixteen weeks with live review sessions and peer collaboration.
- Data preparation and pipeline building
- Classification, regression, and clustering
- Model evaluation and selection
- Structured code reviews on portfolio projects
- Live review sessions with peers
How the track unfolds
Working with real datasets — cleaning, transforming, understanding
Supervised learning algorithms in practice with Scikit-learn
Model evaluation frameworks and iterative improvement
Portfolio projects with code review and live discussion sessions
Advanced AI Engineering Programme
An in-depth programme covering deep learning, model deployment, and the workflow of taking projects from prototype to a running service. Learners build a substantial capstone with one-to-one mentorship and detailed feedback. Suited to committed learners ready for demanding, project-led study. Spans twenty-four weeks with a structured mentor schedule and a portfolio review at the close.
- Deep learning with PyTorch
- Model deployment and serving APIs
- MLOps workflow and monitoring
- One-to-one mentorship sessions
- Capstone project and portfolio review
How the track unfolds
Deep learning architecture, training, and optimisation
Model deployment with FastAPI and container-based workflows
Capstone build with structured one-to-one mentorship
Portfolio review session and end-of-programme feedback
Which track?
Compare the Tracks
Use this overview to find the track that matches your current level and what you want to do next.
| Foundations | Applied ML | Advanced | |
|---|---|---|---|
| Best for | |||
| Prior coding experience | None needed | Some Python | Comfortable coder |
| Duration | 12 weeks | 16 weeks | 24 weeks |
| Mentor feedback | |||
| Code reviews | Project only | ||
| Live sessions | |||
| One-to-one mentorship | |||
| Capstone / portfolio review | |||
| Price | ฿4,500 | ฿16,000 | ฿34,000 |
Not sure which fits? Send us a message — we can help you decide.
Our standards
Consistent Across All Tracks
Regardless of which track you join, these standards apply throughout.
Privacy & Data
Learner data is handled in compliance with Thailand's PDPA. No data shared with advertisers or third-party platforms for marketing.
Feedback Turnaround
Mentor responses to submitted work are delivered within three working days. For the Advanced track, this includes written notes and a follow-up if needed.
Curriculum Maintenance
Modules are reviewed every six months to stay aligned with current tools and practice. Enrolled learners access updated material at no additional cost.
Support Availability
The support team is reachable Monday to Saturday, 09:00–18:00 Bangkok time. Forum activity is monitored daily and moderated by the Neuronest team.
Accessible Platform
Course material runs in a standard browser — no specialist software, no high-bandwidth video requirements. Works on laptop and mobile.
Your Code, Your Work
All project code you write is yours to keep and use. Neuronest makes no claim over work you produce as part of course projects.
Pricing
Simple, All-In Fees
Foundations
฿4,500
One payment · 12-week access
- All course materials
- Community forum access
- Mentor feedback on project
Applied ML Track
฿16,000
One payment · 16-week access
- All course materials
- Forum + peer collaboration
- Portfolio project code reviews
- Live review sessions
Advanced Programme
฿34,000
One payment · 24-week access
- All course materials
- Forum + live sessions
- One-to-one mentor calls
- Capstone + portfolio review
Have Questions Before You Decide?
Send us a message with any questions about the tracks, the approach, or what to expect. We'll help you figure out where to start.
Get in Touch