AI course solutions at Quanta

// course catalogue

Three Courses, One Clear Path

From your first Python script to a deployed AI system — each course is detailed, hands-on, and supported by a mentor throughout.

Back to Home

// methodology

How the Courses Are Structured

Each course at Quanta is divided into weekly modules, each with a specific topic, reading material, and a practical task. The task is what distinguishes study from comprehension — you're expected to do something with the material, not just read through it.

Reviewed exercises go to a mentor, who reads your submission and writes a response. Not a score. A written paragraph or two that addresses the specific decisions you made and what to consider next. This is slower than automated grading and intentionally so.

Weekly Q&A sessions give you the opportunity to ask about anything from that week — whether it's a specific piece of code, a conceptual question, or an issue with how something worked on your machine. The sessions are recorded for those who can't attend live.

01

Module-based weekly structure

Each week has a clear topic, materials, and a task. You complete it within the window that suits your schedule.

02

Exercises reviewed by a mentor

Selected exercises are sent to a mentor who provides written feedback — not a rubric, but a real response to your specific work.

03

Weekly live Q&A

Scheduled sessions with a named mentor. Recordings available. You can ask anything from that week — technical, conceptual, or environmental.

04

Quarterly content updates

Course materials are reviewed every quarter. Outdated tools or workflows are updated before the next cohort starts.

// course 01 — beginner

Intro to AI Development

An introductory online course covering programming and data basics for machine learning, taught through small hands-on tasks. Designed for learners with some coding familiarity who haven't worked with data or ML before.

What you'll work through:

Python for data: lists, dictionaries, loops, functions with data in mind
Working with pandas: loading, cleaning, and inspecting tabular data
Exploring and visualising datasets before modelling
Building a first classification model with scikit-learn
Reading and interpreting evaluation metrics

DURATION

8 weeks

PACE

Self-paced

PRICE

฿4,100

LEVEL

Beginner

Enquire About This Course
Intro to AI Development course
Machine Learning Workshop course

// course 02 — intermediate

Machine Learning Workshop

An intermediate course on preparing data and building and evaluating models on realistic datasets. Designed for learners who have covered the basics and want to develop practical, independent ML skills.

What you'll work through:

Data cleaning and feature engineering on messy, real-world datasets
Supervised learning: regression, classification, ensemble methods
Model selection, cross-validation, and avoiding overfit
Unsupervised methods: clustering and dimensionality reduction
Honest model evaluation and communicating results

DURATION

12 weeks

PACE

Mentor-guided

PRICE

฿16,000

LEVEL

Intermediate

Enquire About This Course

// course 03 — advanced

AI Deployment Engineering Track

A comprehensive track on deploying and maintaining dependable AI systems, organised around a portfolio project. For committed learners building toward independent work with AI in production environments.

What you'll work through:

Serving ML models with FastAPI — structuring, versioning, and endpoint design
Containerising applications with Docker for consistent environments
Monitoring model performance and detecting data drift in production
Infrastructure basics and deployment pipelines
Portfolio project: full system built, documented, and code-reviewed

DURATION

16 weeks

PACE

Project-based

PRICE

฿34,000

LEVEL

Advanced

Enquire About This Course
AI Deployment Engineering Track

// which course

Choosing the Right Course

Use this to find your starting point. When in doubt, reach out — we'll discuss your background and help you decide.

Feature Intro
฿4,100
ML Workshop
฿16,000
Deployment
฿34,000
Duration 8 weeks 12 weeks 16 weeks
Prior coding needed Light familiarity Intro course or equivalent ML Workshop or equivalent
Mentor feedback on exercises
Weekly Q&A sessions
Code review sessions
Portfolio project
Best for Starting from scratch with AI/ML Building practical ML skills Working toward production systems

// standards

What All Three Courses Share

Regardless of level, these elements are consistent across the full catalogue.

Data Security

Learner information and exercise submissions are held securely. Nothing shared beyond what's required to deliver the course.

Quarterly Reviews

All materials reviewed every quarter. Content is updated when practices change, not left to become stale over successive cohorts.

Named Instructors

Every course has a specific instructor responsible for the material and the sessions. You know who you're learning from.

Real Tools Only

No proprietary platforms or simplified stand-ins. All tools used in courses are open-source, freely available, and actually used in practice.

Clear Prerequisites

Each course states exactly what background is expected before enrolment. No surprises mid-course about knowledge you were assumed to have.

Responsive Support

Email responses within one business day during office hours. Questions submitted during active course periods receive priority handling.

// pricing

Course Fees

One-time fee per course. All materials, datasets, and mentor feedback included. No add-ons.

// Beginner

Intro to AI Dev

฿4,100

  • 8 weeks, self-paced
  • Starter notebooks included
  • Reviewed exercises
  • Weekly Q&A sessions
Enquire

// Advanced

Deployment Track

฿34,000

  • 16 weeks, project-based
  • Project framework included
  • Code review sessions
  • Portfolio project output
Enquire

// questions?

Not Sure Which Course to Start With?

Get in touch with a few words about your background and what you're hoping to build. We'll suggest the right starting point and answer any questions about the curriculum.

Get in Touch