Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Classification

The video introduces the concept of classification in machine learning using images as examples. It explains how images are represented as matrices of numbers in computers and discusses the common features that define categories like dogs. The video also covers feature engineering, where specific attributes are identified to distinguish between categories. Finally, it outlines the classification process, where a machine learning model predicts the category of an object based on its features.

What is the primary way images are represented in computers?

• As sequences of text

• As matrices of numbers

• As audio signals

• As video frames

What commonality allows different images of dogs to be classified under the same category?

• They share certain features

• They are all the same size

• They are all taken with the same camera

• They all have the same color

What is the term for manually identifying features that define a category?

• Feature extraction

• Feature engineering

• Feature selection

• Feature mapping

Which of the following is an example of a feature that might define a dog?

• The color of its fur

• The sound of its bark

• The distance between its eyes

• The length of its tail

What is the main goal of a machine learning classification model?

• To store images efficiently

• To enhance image quality

• To predict the category of an object

• To generate new images

In the context of classification, what is a 'label'?

• A type of image filter

• A category assigned to an object

• A method of data compression

• A style of image editing

What does a classifier model do with an image input?

• It predicts the image's category

• It enhances the image resolution

• It changes the image format

• It compresses the image