Pintar Studio course overview
Our Courses

Three Courses. One Clear Path Through AI Development.

From writing your first Python function to building applications with language models, Pintar Studio's courses form a connected sequence. Each course works on its own — but each one also prepares you for the next.

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How We Teach

The Pintar Studio Methodology

Set the Context

Each module opens with a clear "Hands-On This Week" panel — a plain-language statement of what you will build that week and why it matters.

Do the Work

Reading is kept short. Most of each module is spent writing and running code, encountering problems, and solving them — with reference materials available.

Submit and Receive Feedback

Your submissions are reviewed by a tutor. Feedback explains what you did well, what needs improvement, and why — then you move to the next module.

Coding Basics for AI course
Course 1

Coding Basics for AI

RM 145 6 weeks · Beginner

A friendly start in Python and the tools used across AI work, taught through small, practical tasks. Suited to those newer to coding who learn best by doing. Includes weekly hands-on exercises and tutor support over six weeks, at a comfortable pace.

  • Python fundamentals: variables, loops, functions, file handling
  • Introduction to common AI libraries (numpy, pandas basics)
  • Weekly practical tasks with tutor feedback
  • Setup guide included — no prior software knowledge needed

How the Six Weeks Work

1

Environment setup and first Python scripts

2

Data types, control flow, and functions

3

Reading and writing data files with Python

4

Introduction to numpy arrays and pandas DataFrames

5

Working with a real dataset end-to-end

6

Final practical exercise and tutor review

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Course 2

Applied Machine Learning

RM 600 10 weeks · Intermediate

A practical course building and evaluating models on real datasets, with honest discussion of what works and what does not. Best for those with basic programming. Includes graded projects and code feedback across ten weeks.

  • Supervised learning with scikit-learn
  • Feature engineering and model evaluation
  • Two graded projects with individual code feedback
  • Honest comparison of model approaches on real data

Ten-Week Structure

1–2

Data exploration and cleaning with pandas

3–4

Linear and tree-based models — how they work and when to use each

5–6

Model evaluation: cross-validation, metrics, and what they mean

7–8

First graded project with tutor feedback

9–10

Pipeline building and final project submission

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Applied Machine Learning course
Generative AI Workshop course
Course 3

Generative AI Workshop

RM 415 12 weeks · Intermediate–Advanced

A hands-on track building small applications with modern language models, taught responsibly and step by step. Intended for learners comfortable with Python. Includes a mentored project with careful, constructive review over twelve weeks.

  • Working with language model APIs in Python
  • Prompt design, output handling, and testing
  • Responsible use: understanding where models fail
  • Twelve-week mentored project with constructive review

Twelve-Week Structure

1–3

Language model concepts, API setup, first requests

4–6

Prompt design patterns and output parsing

7–8

Building a small application: design, implementation, testing

9–10

Responsible AI: evaluating reliability, handling errors

11–12

Final project completion with mentor feedback and review

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Choosing the Right Course

Which Course Fits Your Starting Point?

Use this table to identify the course that matches your current level. Not sure? Send us a message — we can help you decide.

Factor Coding Basics Applied ML Generative AI
Prior coding needed None Basic Python Comfortable Python
Duration 6 weeks 10 weeks 12 weeks
Price RM 145 RM 600 RM 415
Tutor feedback
Graded projects Exercises only
Mentored final project
Best for Complete beginners to coding Python users new to ML Python users ready to build AI apps
Across All Courses

Standards Shared Across Every Course

Data Protection

Learner information is handled under Malaysia's PDPA. No personal data is shared with third parties for marketing purposes.

Curriculum Maintenance

Materials are reviewed before each cohort. When library versions or API documentation changes, course references are updated accordingly.

Performance Expectations

Each course states clearly what you should be able to do by the end. Progress is measurable through the exercises and project submissions.

Support Response

Tutor responses to questions within two working days. Learners are given a clear point of contact throughout the course.

Accessible Setup

All courses begin with an environment setup guide. Software used is free and open-source. Any standard laptop running Windows, macOS, or Linux is sufficient.

Responsible AI Teaching

All courses discuss AI limitations alongside capabilities. Learners leave understanding both what the tools can do and where they should not be trusted without checking.

Course Pricing

Transparent Pricing in Malaysian Ringgit

All prices are per-course. No subscription or recurring fees. Payment options are shared after you submit an enquiry.

Coding Basics for AI

6 weeks · Beginner

RM 145

per course enrolment

  • Weekly exercises with feedback
  • Tutor email support
  • Environment setup guide
  • Completion document
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Most comprehensive

Applied Machine Learning

10 weeks · Intermediate

RM 600

per course enrolment

  • Graded projects with code review
  • Individual tutor feedback
  • Real dataset exercises
  • Two graded projects
  • Completion document
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Generative AI Workshop

12 weeks · Int.–Advanced

RM 415

per course enrolment

  • Mentored final project
  • Constructive tutor reviews
  • Language model API work
  • Responsible AI module
  • Completion document
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Not Sure Where to Start?

We Will Help You Choose the Right Course

Send us a message with a brief description of your current experience and what you are hoping to work towards. We will suggest the course that makes the most sense.

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