🧠 Topic Summary
Unplugged activities are a fantastic way to learn core programming and machine learning concepts without writing a single line of code! They use physical objects and everyday scenarios to illustrate ideas such as algorithms, data representation, and pattern recognition. This approach helps build intuition and understanding before diving into the complexities of Python syntax.
This worksheet uses an unplugged approach to introduce Python concepts in Machine Learning, emphasizing hands-on activities and conceptual understanding before diving into the code itself. You'll work through exercises that mimic how a computer learns, using real-world examples that are easy to grasp.
🧮 Part A: Vocabulary
Match the following terms with their definitions:
| Term |
Definition |
| 1. Algorithm |
A. A set of data used to train a machine learning model. |
| 2. Feature |
B. A step-by-step procedure for solving a problem. |
| 3. Model |
C. A characteristic or attribute of a data point. |
| 4. Data Set |
D. A representation of a pattern or relationship learned from data. |
| 5. Prediction |
E. An estimate of an outcome based on a model. |
✍️ Part B: Fill in the Blanks
Complete the following paragraph using the words provided (Data, Patterns, Learn, Training, Model).
Machine Learning is about teaching computers to _____ from _____. We feed the computer a ______ dataset, and it tries to identify _____. Once the computer has learned, it builds a _____, which can then be used to make predictions on new data.
🤔 Part C: Critical Thinking
Imagine you're teaching a robot to identify different types of fruit. How would you explain the concept of 'features' (like color, size, shape) to the robot without using a computer?