Three courses. One clear path.
From your first Python notebook to a deployable AI system — each Neuronest course is a self-contained step with its own scope, materials, and support.
← Back to HomeHow the courses are structured
Each course follows the same pattern: a series of titled blocks, hands-on tasks, reviewed submissions, and access to weekly sessions. The content and difficulty change — the format stays consistent.
1. Lesson blocks
Short, plainly titled units covering one idea at a time
2. Practical tasks
Exercises using real data and open-source tools
3. Human feedback
Written notes from a team member on your submitted work
4. Progress forward
Move to the next block once the current one is reviewed
First Steps in AI Coding
An introductory online course covering programming and data basics for machine learning, taught through small hands-on tasks. Designed for learners with light coding familiarity — you should have seen Python before, but do not need to be fluent. Self-paced over eight weeks with weekly question sessions and reviewed exercises. Includes course notes and starter notebooks.
- Working with Python data types, loops, and functions
- Loading and inspecting tabular data with pandas
- Introduction to arrays and numerical operations
- Understanding what machine learning is doing, in plain terms
- Working in Jupyter notebooks from the start
How the course runs
Enrol and receive access to the course platform and starter notebooks
Work through lesson blocks at your own pace, completing exercises after each
Submit exercises for written feedback; join weekly Q&A sessions as needed
Complete all blocks within the eight-week period (extensions available on request)
Hands-On Machine Learning
An intermediate course on preparing data and building and evaluating models on realistic datasets, with careful, honest assessment. For learners who have a foundation in Python and data handling already in place. Runs over twelve weeks with mentor feedback. Includes datasets and walkthroughs covering the full modelling cycle.
- Data cleaning, feature selection, and preparation workflows
- Building classification and regression models with scikit-learn
- Model evaluation: cross-validation, confusion matrices, error analysis
- Understanding when a model is and is not performing well
- Step-by-step walkthroughs of three realistic datasets
How the course runs
Receive access to datasets, notebooks, and the twelve-week block sequence
Work through data preparation and modelling blocks with hands-on exercises
Submit models and analysis notebooks for mentor feedback after each section
Complete the final evaluation task and receive a written assessment
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AI Engineering Portfolio Track
A comprehensive track on deploying and maintaining dependable AI systems, organized around a portfolio project. For committed learners building toward independent work. Runs over sixteen weeks with mentor sessions and code reviews. Includes a project framework, progress record, and a portfolio you keep after the course ends.
- Structuring an AI project from specification to deployment
- Building APIs and packaging models for use by other services
- Monitoring deployed models and handling data drift
- Writing documentation that other engineers can follow
- Mentor-reviewed code for each major project milestone
How the track runs
Receive the project framework and agree on a project scope with your mentor
Work through engineering blocks in the order that suits your project
Attend scheduled mentor sessions and submit code for review at each milestone
Complete the project, receive a written progress record, and keep the portfolio
Which course is right for you?
Use this table to find the course that fits your current background.
| Feature | First Steps ฿4,000 |
Hands-On ML ฿15,800 |
Portfolio Track ฿34,200 |
|---|---|---|---|
| Suitable for beginners | |||
| Requires foundations in place | |||
| Human-reviewed exercises | |||
| Mentor-reviewed code | |||
| Realistic datasets included | |||
| Portfolio project | |||
| Deployment and API content | |||
| Written progress record |
Not sure where to start? Contact us and we will help you choose.
Course standards
What we hold to across all three courses.
Privacy and data handling
Learner data is stored securely. Submitted work is used only for feedback purposes and is not shared externally.
Materials kept up to date
Notebooks and datasets are reviewed before each new cohort. Outdated content is revised, not left to accumulate.
Response time
Email support and exercise feedback are returned within one Thai working day during active cohort periods.
Open-source tooling only
All course tools are open-source and available without a paid licence. Nothing locks you into a platform after the course ends.
Accurate descriptions
Course pages are updated when content changes. What is described is what you get.
Flexible pacing
Timelines are guides, not strict deadlines. Learners who need more time can request an extension without losing access to their materials.
Pricing
All prices in Thai Baht. No hidden costs — the fee covers materials, exercises, feedback, and sessions.
First Steps in AI Coding
฿4,000
one-time · 8 weeks
- Course notes and starter notebooks
- Exercise reviews
- Weekly Q&A sessions
- Email support
Hands-On Machine Learning
฿15,800
one-time · 12 weeks
- Realistic datasets and walkthroughs
- Mentor feedback on submissions
- Weekly Q&A sessions
- Email support
- Instalment option available
AI Engineering Portfolio
฿34,200
one-time · 16 weeks
- Project framework and progress record
- Mentor code reviews
- Scheduled mentor sessions
- Portfolio belongs to you
- Instalment option available
Not sure which course to start with?
Describe your background and what you are hoping to learn. We will suggest the right starting point and tell you what to expect.
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