// our programs
Three Tracks, One Path Forward
Whether you're just starting with Python or working toward deploying your first ML model, there's a Gradient Lab track built for where you are right now.
Back to Home// how we teach
Our Teaching Methodology
Experiment First
Each module opens with a task you run immediately — before any theory is introduced.
Observe & Question
You note what happened, why it might work, and what would change if you altered one thing.
Concept Explained
The underlying idea is introduced once you've seen it in practice — landing much more solidly.
Feedback & Iterate
Projects are reviewed by instructors, you adjust, and the cycle continues with the next module.
// track 01
AI Explorer Course
A friendly introductory course covering Python basics and the core ideas of AI for curious newcomers. Built around small, guided experiments, the Explorer Course helps you move from "I've heard of Python" to "I understand what a model is and I've built something simple." There's a learner community included, so you're not working through it alone.
- No prior coding or math background needed
- Guided Python experiments from the first session
- Practice projects included across all modules
- Community forum with other learners at the same stage
- Estimated duration: 4–6 weeks at a relaxed pace
// what you'll do
- Set up a Python environment and run your first script
- Work through data types, loops, and functions via experiments
- Explore basic AI concepts: datasets, features, predictions
- Build a simple classification model from scratch
- Document your findings in a shareable notebook
// what you'll do
- Work through supervised and unsupervised learning projects
- Submit code for instructor review and apply feedback
- Explore scikit-learn, pandas, and matplotlib in depth
- Build a portfolio of three distinct ML projects
- Participate in collaborative code sessions with the group
// track 02
ML Lab Track
A hands-on track where learners build machine-learning projects with guidance and feedback throughout. Suited to those who already have basic Python skills and want to move into real ML work rather than more tutorials. Includes code reviews from instructors and a growing portfolio of documented projects by the time you're done.
- Requires basic Python (or completion of Explorer)
- Instructor code reviews on every submitted project
- Three portfolio projects by end of track
- Weekly collaborative sessions with the cohort
- Estimated duration: 8–10 weeks
// track 03
Mentored Research Program
A mentor-supported program covering deeper development skills, from model building to deployment fundamentals. Designed for dedicated learners building a portfolio who are ready to go further than guided exercises. Includes individual mentoring sessions and collaborative workshops as part of the program — not as extras.
- Suited to those with ML foundations (or ML Lab completion)
- 1-on-1 mentoring sessions included in the program
- Model building and deployment fundamentals covered
- Collaborative workshops with other research-track learners
- Estimated duration: 12–16 weeks
// what you'll do
- Design and scope a personal AI project with mentor input
- Build and iterate on models with structured feedback
- Cover deployment patterns and environment setup
- Join collaborative workshops and research discussions
- Complete with a documented, deployable project portfolio
// choose your track
Track Comparison
Use this to find the track that matches your current situation.
| Feature | Explorer | ML Lab ★ | Research |
|---|---|---|---|
| Price (฿) | 3,400 | 13,900 | 29,900 |
| Prior coding needed | None | Basic Python | ML foundations |
| Estimated duration | 4–6 weeks | 8–10 weeks | 12–16 weeks |
| Instructor code review | |||
| 1-on-1 mentoring | |||
| Deployment fundamentals | |||
| Community forum | |||
| Best for… | Complete newcomers | Learners with Python basics ★ Most popular | Serious ML portfolio builders |
// shared across all tracks
Technical & Teaching Standards
Learner Data Privacy
Personal and submission data is stored securely and never used for advertising or shared with third parties.
Content Quality Reviews
All modules are reviewed before each cohort. Feedback from previous learners is built into the revision process.
Bilingual Support
Questions submitted in English or Thai are answered by the Chonburi-based team within one Thai business day.
Up-to-Date Tooling
Courses use current Python libraries and ML frameworks. We don't teach workflows built around deprecated tools.
Local Payment Options
PromptPay, Thai bank transfer, and international credit cards accepted. No surprise fees or recurring charges.
Community Access
All tracks include access to the learner community forum — useful for questions, peer review, and progress sharing.
// pricing
Simple, One-Time Pricing
Pay once per track. No subscriptions, no add-ons, no conversion fees. All prices in Thai Baht.
// track 01
AI Explorer Course
- Python fundamentals
- AI core concepts
- Practice projects
- Learner community
- 4–6 weeks
// track 02
ML Lab Track
- ML project work
- Code review by instructors
- Portfolio of 3 projects
- Collaborative sessions
- 8–10 weeks
// track 03
Mentored Research
- 1-on-1 mentoring included
- Deployment fundamentals
- Collaborative workshops
- Deployable portfolio project
- 12–16 weeks
// not sure which fits?
We'll Help You Choose
Send us a message describing your background and what you'd like to be able to do. We'll point you toward the track that makes the most sense.
Get in Touch