// Course Tracks
Three Tracks, One Coherent Path
Gradient offers three sequential courses in AI development, from Python fundamentals through to self-directed optimisation work. Each track has a defined scope and leads naturally to the next.
Back to Home// Methodology
How the Tracks Are Designed
Each course in the Gradient sequence is built around a single organising idea: that meaningful progress in machine learning comes from repeating a cycle — build something, evaluate it honestly, make a specific change, and observe the result. The three tracks embody that cycle at increasing levels of depth and independence.
Content is reviewed quarterly and updated when library versions or standard practices shift. Exercises use real datasets and open-source tooling throughout. The aim is for learners to finish each track with code they can run, read, and explain.
Beginner-safe pacing
Build-measure-refine cycle
Mentor-reviewed projects
Open-source tooling only
// Track 01
Step-by-Step Foundations
A patient beginner track that builds Python and core ideas one small step at a time, with frequent practice. Designed for learners who value a gentle gradient. Finishes with a tidy set of starter projects. Every concept is introduced with context explaining why it matters, not just how to use it.
- Python syntax, data structures, and functions from scratch
- Introduction to NumPy, pandas, and basic data handling
- First supervised learning models with scikit-learn
- Starter project portfolio (3 documented notebooks)
Process
// Track 02
Improving Models in Practice
An intermediate track on the cycle of building, measuring, and refining models, taught through hands-on iteration. Covers honest evaluation and clear reporting. Completed with mentor-reviewed projects. This track is for learners who can write basic Python and want to move from running models to understanding and improving them.
- Train-test splits, cross-validation, avoiding data leakage
- Evaluation metrics: precision, recall, RMSE, and when to use them
- Hyperparameter tuning with a clear rationale
- Written project with mentor feedback (two rounds)
Process
// Track 03
Optimisation & Capstone
A senior track on tuning and improving systems thoughtfully, finishing with a self-directed capstone. Emphasises sound method and documentation. Ends with a presented project for the portfolio. Learners choose a domain and problem they care about and develop the project with structured mentor guidance.
- Advanced optimisation techniques and search strategies
- Documentation practices for reproducible ML work
- Self-directed capstone project in a domain of your choice
- Final presentation and portfolio-ready write-up
Process
// Comparison
Choosing the Right Track
A feature comparison to help you decide where to start.
| Feature | Foundations ฿3,400 |
Improving Models ฿6,800 |
Optimisation & Capstone ฿11,300 |
|---|---|---|---|
| Prior Python required | |||
| Mentor project feedback | |||
| Self-directed capstone | |||
| Portfolio output | 3 starter notebooks | Reviewed project | Presented capstone |
| Typical duration | 6–8 weeks | 8–10 weeks | 10–14 weeks |
| Best for | Complete beginners | Learners with basic Python | Practise-ready learners |
// Across All Tracks
Standards Shared by Every Course
Privacy & Data
Student data is held securely and used only for course delivery. No sharing with third-party advertisers.
Content Reviews
All modules reviewed quarterly. Library and tooling changes reflected before each new cohort begins.
Responsive Support
Questions answered by the course team during Bangkok business hours. Response time typically within one working day.
Portable Outcomes
All code and notebooks belong to the learner. Files run in standard Python environments outside any Gradient platform.
// Pricing
Course Prices in Thai Baht
All prices are one-time per track. No recurring subscription, no hidden fees.
Track 01
Foundations
฿3,400
one-time · self-paced
- Full Python foundations track
- 3 starter projects
- Support team access
- All exercises & notebooks
Track 02
Improving Models
฿6,800
one-time · self-paced
- Intermediate iteration track
- Mentor-reviewed project (x2)
- Evaluation & reporting methods
- Written feedback, not auto-score
Track 03
Optimisation & Capstone
฿11,300
one-time · self-paced
- Advanced optimisation methods
- Self-directed capstone project
- Full mentor guidance
- Portfolio presentation session
// Enrol
Ready to Pick a Track?
Send a message with the track you are interested in and any questions. We will confirm the details and walk you through the next steps.
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