Three Pathways into
AI Development
Each course is designed as a self-contained stop on a longer route — choose where to join based on what you already know, then follow the pathway from there.
← Back to HomeHow the Courses Are Built
Every Cogito course follows the same design logic: introduce a concept in writing, demonstrate it with a worked example, then ask you to apply it with a modified exercise. The explanation comes first — we don't ask you to run code before you know what it does.
Live weekly sessions give you a place to raise what you got stuck on during the week. These aren't lectures — they're question sessions, and the questions drive the agenda.
Assignments on the intermediate and advanced courses are reviewed by a mentor who has worked through the same material. Feedback is specific: it addresses what you submitted, not a generic checklist.
Written First
Concepts in clear prose before any code
Hands-on
Practice notebooks in every module
Live Sessions
Weekly Q&A with the instructor
Revised Regularly
Updated after each cohort
Programming for AI: Starter Course
A beginner-friendly online course covering the programming and data fundamentals needed before working with machine learning, taught through small, practical exercises. For learners new to the field who want a careful start. Self-paced over six weeks with weekly question sessions. Includes notes and practice notebooks.
- Python syntax, data types, and control flow
- Working with files and tabular data
- Basic numerical operations with NumPy and Pandas
- Setting up a local development environment
- Practice notebooks for every topic
How the course runs:
- 1Review written material and worked examples for the week
- 2Complete the practice notebook exercises
- 3Attend the live weekly Q&A session
- 4Repeat across 6 weeks; receive completion record
Machine Learning in Practice
An intermediate course walking learners through preparing data and building and evaluating models on realistic problems, with honest discussion of limitations. For those with basic coding experience. Runs over ten weeks with reviewed assignments and mentor feedback. Includes datasets and recorded walkthroughs.
- Data cleaning, shaping, and feature preparation
- Supervised learning: regression and classification
- Model evaluation and cross-validation
- Handling imbalanced data and overfitting
- Reviewed assignments with written mentor feedback
How the course runs:
- 1Study the written module and watch the walkthrough recording
- 2Work through the notebook exercises using the included dataset
- 3Submit the assignment for written mentor feedback
- 4Attend weekly Q&A and revise if needed; repeat across 10 weeks
AI Systems & Deployment Track
A thorough track on building and deploying dependable AI systems, organized around a portfolio project and sound engineering practice. For committed learners aiming at independent work. Runs over fourteen weeks with mentor sessions and code reviews. Includes a project framework and progress record.
- System design for AI applications
- Model serving, APIs, and deployment pipelines
- Monitoring and maintaining deployed models
- Portfolio project built across the track
- Code reviews and one-to-one mentor sessions
How the course runs:
- 1Study the module and begin the corresponding project section
- 2Submit code for review; receive written and verbal feedback
- 3Attend one-to-one mentor sessions at set points in the track
- 4Complete the portfolio project and progress record over 14 weeks
Which Course Is Right for You?
Choose based on what you already know, not what you want to end up knowing.
| Feature | Starter ฿3,800 |
ML in Practice ฿15,000 |
AI Systems Track ฿33,000 |
|---|---|---|---|
| Prior coding needed | No | Basic Python | ML level |
| Duration | 6 weeks | 10 weeks | 14 weeks |
| Practice notebooks | |||
| Weekly live Q&A | |||
| Reviewed assignments | — | ||
| Real datasets | — | ||
| 1:1 mentor sessions | — | — | |
| Portfolio project | — | — |
Best for: Starter
No prior experience. You want to understand what programming is and build a solid base before anything more advanced.
Best for: ML in Practice
You can write basic Python and want to understand how machine learning actually works — not just how to call a library.
Best for: AI Systems Track
You've built models and want to take them further — into production, engineering practice, and independent project work.
What All Courses Share
Data Privacy
Learner information is used only for course delivery and communication. Not shared with third parties.
Updated Content
Materials are reviewed after each cohort. If a tool or practice changes in the field, the course reflects it.
Clear Prerequisites
What you need to know before joining each course is stated clearly. No hidden requirements.
Honest Scope
Each course states what it covers and what it doesn't. Limitations of methods are discussed, not hidden.
Accessible Support
All enrolment, access, and scheduling questions are answered by a real person within one working day.
Completion Record
Each course issues a completion record when you've finished the required activities.
All-Inclusive Course Prices
The price listed covers everything — no add-ons required.
Programming for AI
- 6-week self-paced access
- All written materials & notes
- Practice notebooks
- Weekly live Q&A
- Completion record
Machine Learning in Practice
- 10-week structured access
- All materials, datasets & walkthroughs
- Weekly live Q&A
- Reviewed assignments
- Written mentor feedback
- Completion record
AI Systems & Deployment
- 14-week structured access
- All materials, project framework & records
- Weekly live Q&A
- Code reviews by mentor
- 1:1 mentor sessions
- Portfolio project you retain
- Completion record
Not Sure Where to Start?
Tell us about your background and what you're hoping to learn. We'll help you find the right entry point — or be honest if the timing isn't right yet.
Send a Message