Page 34 - AI & Machine Learning for Beginners: A Guided Workbook

Models: Mathematical representations created when algorithms
learn from data.

Real-Life Analogy: If an algorithm is like a recipe, a

model is like your "sense" of how to cook after following
many recipes. You understand what flavors work together,
even without specific instructions.
Activity: Draw what you think a machine learning model
might "look like" inside a computer:

Features: Identifying Key Ingredients

In Machine Learning (ML), a feature is a specific attribute or
characteristic of the data that helps an algorithm learn and make
predictions. Features provide valuable information that the ML
model uses to detect patterns and improve accuracy.

Example: Predicting House Prices

If an ML model is trained to predict home values, key features
might include:

Number of Bedrooms – More rooms can increase value.

Square Footage – Larger homes often have higher prices.

Location – Homes in desirable neighborhoods tend to cost more.
Age of the House – Older homes may have different valuations

compared to newer builds.
Another Example: Features of an Apple
Imagine teaching a computer to recognize apples. Here are three
different features that could help the AI identify an apple:
32