Discover how supervised learning is used to classify images, such as identifying dogs in a dataset. Learn the fundamentals of machine learning and prepare for the IBM AI Fundamentals certification exam.
Table of Contents
Question
As part of research team, you are tagging images of animals in a database as either “dogs” or “not dogs”.
What kind of machine learning are you preparing for?
A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
Answer
C. Supervised learning
Explanation
Supervised learning involves humans providing structured data to the machine for classification or prediction. Tagging the images provides structure to the data.
In the given scenario, you are part of a research team tasked with tagging images of animals in a database as either “dogs” or “not dogs”. This process is an example of supervised learning, a fundamental concept in machine learning.
Supervised learning is a type of machine learning where the algorithm learns from labeled training data. In this case, the labeled data consists of images that are already tagged as “dogs” or “not dogs”. The algorithm uses this labeled data to learn the distinguishing features and patterns that differentiate dogs from other animals. Once trained, the algorithm can then classify new, unseen images as either “dogs” or “not dogs” based on the patterns it has learned.
The other options, unsupervised learning and reinforcement learning, do not apply to this scenario:
A. Unsupervised learning: This type of machine learning involves working with unlabeled data, where the algorithm attempts to discover hidden patterns or structures in the data without any predefined labels or categories.
B. Reinforcement learning: In this type of machine learning, an agent learns to make decisions by interacting with an environment and receiving rewards or penalties for its actions. The agent learns to maximize its rewards over time through trial and error.
In summary, tagging images as “dogs” or “not dogs” is an example of supervised learning, where the algorithm learns from labeled training data to classify new, unseen images accurately.
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