Machine Learning: A Practical Introduction

Course contents (13 parts)

1. 1

   Pre-course assessment

   Check your starting knowledge before learning

2. 2

   What is Machine Learning?

   Lesson video

3. 3

   Recognize problem types

   Choose a learning approach based on the task, inputs, outputs, and user.

4. 4

   Prepare data

   Prepare representative data and prevent leakage between training and test sets.

5. 5

   Train and use a baseline

   Compare model performance with a simple baseline on held-out data.

6. 6

   Evaluate errors

   Select metrics based on the different costs of model errors.

7. 7

   Monitor after deployment

   Monitor changes and assign owners, approval, and shutdown procedures.

8. 8

   Applied case study and evidence review

   Inspect a high-accuracy model that never finds the target class

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
    Document0 points1 min

    Implementation planner

    A template for connecting course topics to goals, owners, and evidence of results.

13. 13

    Post-course assessment

    8 scenario-based questions applying the lesson concepts.