WitthayaLabs
AI course learning path

Three Courses, One Path

From First Steps to
a Finished Portfolio

Whether you are entirely new to the field or looking to deepen existing skills, each track is designed to take you somewhere specific — and to do so at a pace that suits your life.

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Our Methodology

How the Courses Are Structured

The three courses at Witthaya Labs form a coherent sequence, though each can also be taken independently depending on your background. The curriculum is arranged as a lattice: concepts introduced in one module form the foundation for what follows, so understanding develops progressively rather than in isolated pieces.

Each track combines written study materials, practical exercises using Python tools, and regular contact with a mentor. The mentor's role is to answer questions, give feedback on exercises, and help you understand where a difficulty is coming from — not to deliver one-way instruction.

Written Materials

Practical Exercises

Mentor Check-ins

Ongoing Q&A

Getting Started with AI

Track 01

Getting Started with AI

A supportive first course in Python, data basics, and the ideas behind machine learning, taught patiently and clearly. Suited to learners new to the field who would like a calm, well-paced introduction. Mentors are glad to help whenever questions arise.

  • Python syntax, variables, and control flow
  • Working with data using Pandas and NumPy
  • Core machine learning concepts and vocabulary
  • First simple models with scikit-learn
  • One-to-one mentor support throughout

Learning Steps

1Python environment setup and first scripts
2Data loading, inspection, and cleaning
3Understanding supervised learning
4Training, evaluating, and interpreting a first model

Track 02

Machine Learning in Practice

A practical track where learners build models, work through datasets, and receive regular feedback in small groups. A good fit for those with some coding experience wishing to strengthen their skills. The pace adapts to fit your other commitments.

  • Classification, regression, and clustering models
  • Feature engineering and selection
  • Model evaluation and cross-validation
  • Small group feedback on real dataset work
  • Introduction to neural network concepts

Learning Steps

1Exploratory data analysis and preparation
2Building and comparing model architectures
3Tuning and validating on held-out data
4Group review and written summary of findings
Machine Learning in Practice
Guided Capstone and Portfolio

Track 03

Guided Capstone & Portfolio

A detailed program in which a mentor supports you through a substantial AI project, ending in a portfolio piece and guidance on next steps. Suited to committed learners preparing for further opportunities. Support is steady from start to finish.

  • Project scoping with mentor guidance
  • Independent research and data work
  • Weekly review sessions throughout the project
  • Final portfolio piece and documentation
  • Guidance on presenting work and next steps

Learning Steps

1Define a project meaningful to your interests
2Gather data, explore, and plan the approach
3Build, iterate, and refine with mentor feedback
4Document, present, and discuss next directions

Decision Guide

Which Track Is Right for You?

Feature Track 01
฿1,800
Track 02
฿5,500
Track 03
฿9,600
Best for Complete beginners Some coding experience Committed learners
Python taught
Model building Introductory
Group feedback sessions
Portfolio project
Dedicated mentor

Across All Courses

Standards We Hold in Every Track

Learner Privacy

Personal data shared during enrolment or in sessions is handled carefully and never used for purposes outside the course relationship.

Material Accuracy

Course content is reviewed regularly and updated when tools or best practices shift in ways that would affect what learners need to know.

Responsive Support

Questions sent outside of live sessions receive a response within one working day. Confusion is addressed promptly so it doesn't compound.

Honest Scope

Each course description accurately reflects what is covered. If a particular learning goal falls outside our scope, we will say so directly and without apology.

Inclusive Language

Materials and sessions use plain language first. Technical terms are introduced only when the underlying concept has already been established.

Feedback Loop

Check-in prompts after each module invite learners to say what is working and what is not. This information shapes how sessions are adjusted going forward.

Pricing

Clear Pricing, No Hidden Fees

Each course is a single payment covering all materials, mentor sessions, and access for the full duration. Prices are in Thai Baht.

Track 01

Getting Started with AI

฿1,800

Per course, one payment

  • All written materials
  • Mentor Q&A support
  • Practical exercises
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Track 02

Machine Learning in Practice

฿5,500

Per course, one payment

  • All Track 01 content
  • Small group sessions
  • Real dataset projects
Enquire

Track 03

Guided Capstone & Portfolio

฿9,600

Per course, one payment

  • Dedicated mentor, full project
  • Portfolio-ready outcome
  • Next steps guidance
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Not sure which track to start with?

Write to us with a little background about your experience and what you're hoping to work toward. We'll suggest the most fitting starting point — no pressure, just a helpful conversation.

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