OUR COURSES
Three Courses. One Coherent Path Through AI Development.
From first principles in machine learning to working with language data and running models in production — each track is designed to connect clearly to the next.
← Back to HomeHow We Structure the Learning
Each course at Rianroo follows the same general pattern: introduce a concept, demonstrate it clearly, then put it into practice through a guided task or build. Theory is never introduced purely for its own sake — it is always in service of something you will do.
Concept Introduction
Each new idea is explained in plain language with a concrete example before any code appears.
Guided Demonstration
A worked example shows how to apply the concept in a real scenario, step by step.
Practice Task
Learners apply what they have seen in a structured exercise with clear criteria for completion.
Review & Proceed
A brief consolidation before moving to the next module confirms understanding and catches any gaps.
Foundations of Machine Learning
A gentle grounding in the ideas and tools behind machine learning, taught step by step with supportive practice. A comfortable starting point for learners new to the field. This course covers the core concepts that underpin all machine learning work — supervised and unsupervised learning, how models are trained and evaluated, and how to work with real data in Python.
- Core ML concepts and vocabulary explained accessibly
- Hands-on exercises using Python and scikit-learn
- Data preparation and exploratory analysis techniques
- Model evaluation and how to read the results
- Suitable for learners without a formal CS background
What You'll Work Through
How machines learn from data — the core ideas without the jargon
Preparing and exploring a dataset for use in a model
Training and evaluating a classification model
Regression tasks and reading model performance clearly
Completing a guided end-to-end ML project
What You'll Work Through
Text preprocessing and working with tokenised Thai and English data
Sentiment classification using both classical and transformer approaches
Named entity recognition and sequence labelling
Working with pre-trained language models in a guided build
Completing an NLP mini-project of your own choosing
Natural Language Processing Projects
A hands-on track exploring language models and text projects through small, guided builds. Suited to learners who have a working knowledge of ML fundamentals and are ready to apply their skills to real, manageable tasks with language data. The course addresses Thai text specifically — which is often an afterthought in NLP courses designed for English-language markets.
- Language model exploration with Hugging Face and similar tools
- Thai text processing and tokenisation specifics
- Text classification, NER, and summarisation tasks
- Guided project builds with assessment criteria
- Prerequisite: Foundations of Machine Learning
Model Deployment & MLOps
A calm, practical look at taking models from notebook to a working service, with clear steps and steady guidance. This course covers the gap that many data science learners encounter — knowing how to train a model but not how to make it run reliably for others to use. Helpful for those who want their projects to function in the wider world, not just on their own machine.
- Packaging models as REST APIs with FastAPI
- Containerisation basics with Docker
- Monitoring and logging deployed models
- Versioning and reproducibility in ML workflows
- Suitable after either ML Foundations or NLP Projects
What You'll Work Through
Structuring a project for deployment from the start
Building a simple API around a trained model
Containerising the service with Docker
Monitoring predictions and handling model drift
Deploying to a cloud environment in a guided capstone
Which Course Is Right for You?
Use this table to see how the courses differ and find the best starting point for where you are now.
| Feature | Foundations of ML | NLP Projects | Deployment & MLOps |
|---|---|---|---|
| Prior AI knowledge needed | None required | ML Foundations | ML or NLP course |
| Primary focus | Concepts & fundamentals | Language data & models | Deployment & operations |
| Includes Thai language content | |||
| Includes production/API work | |||
| Best for | Beginners in AI | Building NLP projects | Shipping model services |
| Price (฿ THB) | ฿1,900 | ฿5,600 | ฿3,400 |
Standards Shared Across All Courses
Data Privacy
Learner information is handled with care and not shared for marketing. See our Privacy Policy for full details.
Current Tooling
Course examples use libraries in active use in the field, reviewed and updated at regular intervals.
Direct Support
Reach the team by phone or email during office hours. Responses aim to be substantive, not generic.
Task-Based Assessment
Completion criteria are based on completing guided exercises, not time-on-platform or quiz scores alone.
Readable Materials
Course writing is reviewed for clarity before publishing. If a section is consistently confusing, it is rewritten.
Transparent Fees
All prices are shown in Thai Baht, inclusive of access to the full course material and support during study.
Course Pricing
One-time course fee. No subscriptions. Full access on payment.
COURSE 01
Foundations of ML
฿1,900
one-time fee
- Full course access
- Practice exercises included
- Email & phone support
- Completion record
COURSE 02
NLP Projects
฿5,600
one-time fee
- Full course access
- Guided NLP project builds
- Thai language NLP content
- Completion record
COURSE 03
Deployment & MLOps
฿3,400
one-time fee
- Full course access
- Docker & API deployment
- Cloud deployment capstone
- Completion record
Have Questions Before You Start?
We are happy to talk through which course makes most sense for where you are now. Contact us before you enrol — there is no pressure involved in asking.
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