How AI Learns: The Essential Concepts

Course contents (12 parts)

1. 1

   Pre-course assessment

   Check your starting knowledge before learning

2. 2

   What is Machine Learning?

   Lesson video

3. 3

   Understand AI and learning models

   Distinguish AI, machine learning, and generative AI, and consider what each system can do.

4. 4

   Data provides learning examples

   Understand the role of data quality, coverage, and suitability.

5. 5

   Train, test, and deploy

   Follow the path from examples to a model and distinguish test results from real-world use.

6. 6

   Why AI can be wrong

   Recognize uncertainty, bias, and answers that sound credible but lack evidence.

7. 7

   Choose with understanding

   Use capabilities, limitations, and accountability as criteria when selecting a tool.

8. 8

   Applied case study and evidence review

   Split data without producing an unrealistically favorable test result

9. 9
   Document0 points1 min

   Course study guide

   A practical guide to the course lessons, with Thai and English editions.

10. 10
    Document0 points1 min

    Practical workbook

    Course-based exercises for review and planning a small real-world trial.

11. 11
    Document0 points1 min

    Quick reference

    A concise reference to course topics and review points for use while working.

12. 12

    Post-course assessment

    Review the course’s key ideas.