All tracks
Three tracks, one clear progression
Each Codeloom track is self-contained and builds on the one before it. You can start at the beginning or enter where your background fits.
← Back to HomeHow we teach
The Codeloom methodology
Every track follows the same underlying approach: introduce a concept, use it immediately in a small exercise, then apply it in a project that requires you to make decisions. Feedback is written and personal.
Introduce with context
Each concept is introduced alongside a real reason for it. You know why you are learning it before you write the first line.
Build immediately
Every concept is followed by a build-along exercise. Understanding is tested by producing something that runs.
Submit and get feedback
Projects are reviewed by an instructor who returns specific written notes. You revise if needed, then move on.
Track 01 · Foundation
Programming for AI
A foundation track on Python, version control, and the everyday tools developers use, taught through small build-along exercises. Designed for committed beginners. Learners finish comfortable writing and organising tidy, working code.
What you will work through
Python environment and first programs
Installing tools, understanding the interpreter, writing and running your first complete scripts
Data structures and control flow
Lists, dictionaries, loops, and conditions — practised through small programs with real logic
Functions, modules, and organisation
Writing reusable code, structuring projects, importing and using external libraries
Version control with Git
Tracking changes, creating commits, understanding branching as a working habit rather than an advanced topic
Key outcomes
- Comfortable writing and organising Python code independently
- Working knowledge of Git for version control
- Understanding of how code is structured in real projects
- Readiness to enter the ML projects track
8–10 weeks at a comfortable pace (several hours per week)
No prior programming experience required
Track 02 · Intermediate
Practical Machine Learning Projects
An intermediate, project-led track where learners frame problems, build models, and report results clearly. Covers sensible workflows and honest evaluation. Several guided projects are completed with mentor feedback.
What you will work through
Problem framing and data exploration
Turning a vague question into a well-formed ML problem; understanding your data before touching a model
Model building and selection
Using scikit-learn for classification and regression; understanding why you would choose one approach over another
Honest evaluation
Choosing the right metrics for the problem; documenting what your model does and does not do well
Guided projects with feedback
Several complete projects submitted for written mentor review; revisions encouraged
Key outcomes
- Ability to work through a complete ML project independently
- Portfolio of reviewed, working ML projects
- Discipline around evaluation and documentation
- Readiness to enter the deployment track
10–12 weeks at a comfortable pace
Completion of Track 01, or equivalent Python knowledge verified with our team
Track 03 · Senior
Deployment & Capstone
A senior track on packaging models into reliable applications and a substantial capstone of the learner's design. Emphasises sound engineering and clear documentation. Ends with a presented project for the portfolio.
What you will work through
Packaging models into applications
Building APIs around models; handling inputs, outputs, and errors in a way other systems can rely on
Engineering and documentation
Writing the code and documentation that makes deployed software maintainable over time
Capstone design and build
You choose the problem and design the solution; mentors guide without directing. This is your project.
Capstone presentation
A structured presentation of your project — its purpose, design decisions, results, and limitations
Key outcomes
- A deployed ML application built and documented by you
- A substantial capstone project for your portfolio
- Presentation experience explaining real technical decisions
- Completion record from Codeloom
Approx. 12 weeks including capstone build and presentation
Completion of Track 02, or intermediate ML experience verified with our team
Which track fits you?
Choose your starting point
| Feature | Track 01 ฿4,100 |
Track 02 ฿7,000 |
Track 03 ฿11,200 |
|---|---|---|---|
| Prior Python needed | None | Track 01 or equivalent | Track 02 or equivalent |
| ML model building | |||
| Deployment and packaging | |||
| Self-designed capstone project | |||
| Mentor feedback on projects | |||
| Version control from day one |
Not sure where to start? Send us a message and we will help you work it out.
Across all tracks
Standards that apply to every course
Learner data privacy
Enrolment data is used only for course administration. Nothing is sold or shared.
Annual content review
Track content is reviewed every year to keep tools and approaches current.
Written feedback standard
Every submitted project receives written, specific feedback within five working days.
Direct access to the team
Phone and email contact during office hours. No automated gatekeeping.
Pricing
Clear fees, no subscriptions
Each track is a single payment in Thai baht. No monthly charges, no upsells, no surprises.
Track 01
Programming for AI
฿4,100
- Python and Git foundations
- Build-along exercises throughout
- Project feedback included
- Completion record
Track 02 · Popular
Practical ML Projects
฿7,000
- Problem framing through evaluation
- Multiple guided ML projects
- Written mentor feedback
- Portfolio-ready project outcomes
Track 03 · Senior
Deployment & Capstone
฿11,200
- Model packaging and deployment
- Self-designed capstone project
- Capstone presentation
- Senior-level completion record
Not sure which track is the right fit?
Get in touch and we will ask a few questions about your background and what you want to build. The conversation takes ten minutes and there is no obligation to enrol.
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