Three Tracks, One Direction
Whether you are starting from scratch or extending what you already know, each course is structured to move at a pace that makes the learning stick.
← Back to HomeHow We Structure Learning at Wichaset
Each course at Wichaset follows the same underlying rhythm: introduce a concept in plain language, demonstrate it with a concrete example, then give students time to work with it themselves before moving on. This cycle repeats session by session, building knowledge layer by layer rather than piling information at once.
Sessions run twice a week in the evenings, keeping the pace manageable for those who work full-time. Every module includes a short assignment that prepares students for the next session and gives instructors a chance to spot and address gaps before they become bigger obstacles.
Understand First
Concepts are explained in plain terms before any code is written.
Then Build
Students apply concepts in guided coding exercises during the session.
Review and Continue
Short assignments between sessions reinforce what was covered and flag questions for next time.
Foundations of Machine Learning
A patient introduction to the building blocks of machine learning, with guided exercises and space to ask questions. This track starts from the very beginning — Python syntax, data handling with Pandas, and the statistical ideas that underpin every machine learning model — and progresses steadily through supervised learning algorithms including linear regression, decision trees, and k-nearest neighbours.
What this course covers:
- Python for data work — NumPy, Pandas, Matplotlib
- Core ML concepts — bias, variance, overfitting, cross-validation
- Supervised learning algorithms and when to use each
- Model evaluation and basic feature engineering
- Final project: train and evaluate a model on a real dataset
How the sessions progress:
Python and data foundations (sessions 1–3)
Core ML theory and first models (sessions 4–7)
Evaluation and improvement (sessions 8–10)
Final project and review (sessions 11–12)
Computer Vision Track
A practical track exploring image understanding through small, achievable projects and clear walkthroughs. Students learn how computers read and interpret visual data — covering image preprocessing, colour spaces, edge detection, and convolutional neural network fundamentals — through exercises that produce visible, testable results at each stage.
What this course covers:
- Image data handling with OpenCV and Pillow
- Preprocessing, augmentation, and dataset preparation
- Convolutional neural networks — architecture and training
- Transfer learning using pre-trained models
- Final project: build an image classifier on a chosen dataset
How the sessions progress:
Image data and preprocessing fundamentals (sessions 1–4)
CNN architecture and training (sessions 5–9)
Transfer learning and model tuning (sessions 10–13)
Final project, review, certificates (sessions 14–16)
Applied Deep Learning
A hands-on programme building neural networks through approachable projects and plain explanations. This track covers the full lifecycle of a deep learning project: data preparation, architecture design, training and debugging, evaluation, and the steps involved in making a trained model usable outside of a notebook environment.
What this course covers:
- Neural network fundamentals and PyTorch basics
- Feedforward networks, activation functions, and backpropagation
- Regularisation, dropout, and training strategies
- Introduction to RNNs and sequence modelling
- Final project: design, train, and evaluate a neural network
How the sessions progress:
Deep learning concepts and PyTorch setup (sessions 1–3)
Building and training networks (sessions 4–8)
Advanced techniques and debugging (sessions 9–12)
Final project, review, certificates (sessions 13–16)
Choose the Right Course for You
Not sure which track to start with? This table summarises what each course assumes and what you will be able to do at the end.
| Feature | ML Foundations | Computer Vision | Applied Deep Learning |
|---|---|---|---|
| Prior knowledge needed | None | ML basics | Python + ML basics |
| Duration | ~6 weeks | ~8 weeks | ~8 weeks |
| Price (THB) | ฿2,000 | ฿5,500 | ฿4,100 |
| Hands-on project | |||
| Session recordings | |||
| Best for | Complete beginners | Learners interested in image AI | Those ready for neural networks |
Unsure which track suits your background? Contact us and we will help you decide.
Standards Shared Across All Courses
Student Data Privacy
Enrolment details are stored securely and used only for course administration. We do not share information with third parties.
Post-Course Feedback
We collect structured feedback at the end of every cohort and use it to refine content and delivery before the next intake.
No Upselling Policy
The fee paid at enrolment covers everything listed in the course description. We do not offer paid add-ons during or after the course.
Reviewed Materials
Course materials are reviewed before each new cohort begins to ensure the tools and libraries covered remain current and relevant.
Between-Session Support
Students can send questions by email between sessions. Instructors aim to respond within one working day.
Completion Certificates
Formal Wichaset certificates are issued to students who complete all sessions and submit the final project, documenting the course and skills covered.
Course Fees
All prices in Thai Baht. Each fee covers the full course with no additional charges.
ML Foundations
฿2,000
One payment before course starts
- 12 sessions (~6 weeks)
- Session recordings
- Final project feedback
- Completion certificate
Computer Vision
฿5,500
One payment before course starts
- 16 sessions (~8 weeks)
- Session recordings
- Final project feedback
- Completion certificate
- OpenCV + CNN deep dive
Applied Deep Learning
฿4,100
One payment before course starts
- 16 sessions (~8 weeks)
- Session recordings
- Final project feedback
- Completion certificate
- PyTorch neural networks
Not Sure Which Course to Start With?
Tell us a little about your background and what you would like to learn. We will help you find the right starting point.
Get in Touch