Three Courses,
One Connected Path
From first steps in Python and statistics to deploying models and building a portfolio — every course fits together by design.
Back to HomeHow the Curriculum Is Structured
Each course in the Neuronest catalogue is a distinct layer in a connected learning structure. You can join at whichever level fits your background, and progress through the sequence as your skills develop.
The three courses were designed together — not assembled from separate parts. This means the terminology, tooling, and project approaches stay consistent across all three, so context built in one course transfers directly to the next.
Foundations of Machine Learning
An approachable starting course covering Python fundamentals for data work, core statistics, and the basic ideas behind common models. Designed for newcomers and career changers who want a calm, structured introduction.
What's Included
- Python for data work — from basics to working with datasets
- Core statistics relevant to model understanding
- Introduction to common ML model types
- Guided lessons and small practice projects
- Mentor-supported feedback throughout
- Certificate of completion
Study Path
- 1 Python essentials — data types, control flow, functions, libraries
- 2 Working with data — Pandas, NumPy, basic visualisation
- 3 Statistics for ML — distributions, correlation, inference
- 4 Supervised learning — regression and classification fundamentals
- 5 Model evaluation and a guided final project
Applied Deep Learning Studio
A project-focused course building practical skills with neural networks, modern frameworks, and real datasets. Suited to learners with basic Python who want hands-on experience.
What's Included
- Neural network fundamentals and architecture design
- Modern frameworks — PyTorch or TensorFlow
- Working with real datasets across multiple domains
- Weekly build sessions with instructor guidance
- Code reviews and feedback on your submissions
- Capstone project and ongoing community access
Study Path
- 1 Deep learning foundations — layers, activations, backpropagation
- 2 CNNs for image work — building, training, and evaluating
- 3 Sequence models — RNNs, LSTMs, and transformer basics
- 4 Training at scale — optimisation, regularisation, debugging
- 5 Capstone project with mentor review and community presentation
AI Engineering Career Track
An extended program covering model development, deployment practices, and team workflows, with portfolio-building throughout. Aimed at committed learners preparing for technical roles.
What's Included
- Model development pipelines and MLOps fundamentals
- Deployment practices — APIs, containers, cloud basics
- Team workflows and version control for ML projects
- Mentor guidance throughout the six months
- Applied projects building a documented portfolio
- Career-readiness sessions covering skills and portfolio quality
Study Path
- 1 ML engineering practices — reproducibility, pipelines, monitoring
- 2 Serving models — REST APIs, Docker, cloud deployment
- 3 Applied project 1 — end-to-end build with mentor feedback
- 4 Team collaboration, code quality, and documentation
- 5 Applied project 2 — portfolio-ready with career-readiness review
Which Course Is Right for You?
| Feature | Foundations | Deep Learning Studio | Career Track |
|---|---|---|---|
| Best for | No prior AI experience | Basic Python, wants practical DL | Preparing for technical roles |
| Duration | 8 weeks | 12 weeks | 6 months |
| Mentor feedback | |||
| Capstone project | |||
| Portfolio building | |||
| Career-readiness sessions | |||
| Price (THB) | ฿4,200 | ฿17,500 | ฿34,500 |
Shared Across All Three Courses
Data Privacy
Student records are stored securely and handled in line with PDPA requirements. No data is shared with third-party marketers.
Regular Curriculum Review
Materials are reviewed each intake cycle. If a framework or practice has shifted significantly, the relevant module is updated before the next cohort begins.
Accessible Support
All learners have access to the student support team for logistical and administrative questions. Mentor feedback is scoped to course content.
Verifiable Certificates
Completion certificates include a reference ID. Employers or other institutions can contact us to verify certificate authenticity.
Feedback Cycle
Student feedback collected at module and course end is reviewed by the curriculum team and used in the next intake update.
Managed Cohort Sizes
We limit the number of learners per cohort so that mentors can give meaningful attention to each student's work.
Clear Fees, No Hidden Extras
One price covers everything in each course. Payment details and intake dates are shared on enquiry.
Foundations of ML
- All course materials
- Mentor-supported feedback
- Practice projects
- Completion certificate
Applied Deep Learning
- All course materials
- Weekly build sessions
- Code reviews
- Capstone project
- Community access
AI Engineering Career Track
- All course materials
- Mentor guidance throughout
- Applied projects & portfolio
- Peer collaboration
- Career-readiness sessions
Have Questions Before Deciding?
We're happy to talk through which course fits your background. No obligation to enrol on the first conversation.
Send a Message