Three ways to build depth in AI.
From machine learning foundations to open-source LLM engineering and monthly paper reading — each format designed for a different learning need.
Back to HomeOur Teaching Approach
Every Neuronest course starts from the same premise: the most useful way to learn a technical subject is to read carefully, work through examples in code, and check your understanding against a reference you can return to later.
We do not use video because video is difficult to annotate, slow to scan, and hard to search. We write in long-form prose, structured like the documents practitioners already use — notebooks, documentation, and papers.
Each week introduces a defined set of concepts, paired with a working notebook and an exercise set. The exercise set tests understanding, not memorisation. The reference solutions show one clear way to approach each problem, with explanation of the reasoning.
Quality assurance
All material is reviewed by a second instructor before each intake. Notebooks are run against a clean environment to confirm they execute without error.
Content lifecycle
Course notes and notebooks are reviewed twice per year. When library APIs change or a standard approach shifts, affected sections are updated before the next intake opens.
Student access
Enrolled students retain access to all material for their course after completion. Forum threads remain searchable. New versions of the notes are provided when major updates occur.
Course 1 of 3
Foundations of Machine Learning
A twelve-week reading and exercise course covering the foundations of machine learning — supervised and unsupervised learning approaches, common loss functions, gradient-based optimization, and basic model evaluation. The course uses Python and freely available libraries. Materials include written notes, worked example notebooks, and end-of-week exercises with reference solutions. Recommended for students with prior programming experience.
Key topics covered
- Supervised learning: regression and classification
- Unsupervised learning: clustering and dimensionality reduction
- Loss functions and gradient-based optimisation
- Model evaluation: bias-variance, cross-validation, metrics
- Practical use of scikit-learn and NumPy
Weekly structure
This course is for you if:
- — You can write basic Python but have not studied ML formally
- — You want to understand what algorithms are actually doing
- — You prefer reading to watching videos
- — You want a graded path that builds on itself each week
Course 2 of 3
This course is for you if:
- — You are comfortable reading Python and shell commands
- — You want to build real applications, not just learn theory
- — You want to understand RAG, fine-tuning, and structured output
- — You prefer hands-on work alongside written explanation
Building With Open-Source Language Models
An eight-week course on assembling small applications around open-source language models — retrieval-augmented patterns, light fine-tuning, and structured output handling. The course is technical and hands-on. Each module includes a working code reference, an exercise set, and a discussion forum thread. Students are expected to be comfortable reading Python and basic shell.
Key topics covered
- Working with Hugging Face models locally
- Retrieval-augmented generation (RAG) patterns
- Light fine-tuning with LoRA and PEFT
- Structured output handling and parsing
- Evaluation of generation quality
Module structure
Course 3 of 3
Reading Group on Machine Learning Papers
A monthly reading group focused on recent machine learning papers and accessible survey writing. Each session includes a moderator-led summary, structured discussion, and reading notes shared in advance. The reading group is intended for students who want a calm rhythm of paper-reading without the structure of a multi-week course.
What each session includes
- Pre-session reading notes sent in advance
- 90-minute moderator-led session monthly
- Structured discussion of the main claims and methods
- Focus on both recent papers and useful survey articles
- Session notes archived for members
Monthly rhythm
This group is for you if:
- — You want to follow the field without a multi-week commitment
- — Papers feel dense and you want help building the reading habit
- — You want a regular, low-pressure discussion context
- — You have completed a structured course and want to stay current
Compare Courses
Choose the option that matches your current level and what you want to focus on.
| Feature | Foundations ฿4,800 |
LM Engineering ฿29,500 |
Reading Group ฿18,000/yr |
|---|---|---|---|
| Duration | 12 weeks | 8 weeks | Monthly (1 session) |
| Written notes | Pre-session only | ||
| Code notebooks | — | ||
| Exercise sets | — | ||
| Live sessions | — | — | |
| Python required | — | ||
| Best for | New to ML | Experienced devs | Staying current |
Standards Across All Courses
PDPA Compliance
Student data is handled under Thailand's Personal Data Protection Act. We collect only what is needed to deliver course access and communicate with enrolled students.
Notebooks Verified Before Each Intake
All example notebooks are executed against a clean Python environment before each course intake to confirm they run without error.
Biannual Content Review
All course content is reviewed twice per year. Sections covering tools or techniques that have changed are updated before the next intake opens.
Instructor Support, Not Automated
Forum threads are monitored by course instructors. Responses come from the people who wrote the material, not from automated systems or third-party teaching assistants.
Secure Course Access
Course materials are accessible via a secured student account. Access is tied to the enrolled student and does not transfer or expire after the course period ends.
Scope Described Accurately
We describe what each course covers, what it assumes, and what students will be able to do after completing it. We do not overstate outcomes.
Course Fees
Single flat fee per course. No subscription. No tiered access levels.
Foundations of ML
- 12 weeks of written notes
- 12 worked example notebooks
- Weekly exercises with solutions
- Module discussion forums
LM Engineering
- 8 weeks of in-depth notes
- 8 complete code references
- Hands-on exercises per module
- Module discussion forums
Reading Group
- 12 monthly sessions per year
- Pre-session reading notes
- Moderator-led discussion
- Session archive access
Not sure which course to start with?
Send us a brief message about your background and we will point you to the right starting point.