Master clustering algorithms for Microsoft’s AI-900 certification with our guide to unsupervised machine learning. Discover exam strategies for handling unlabeled data, comparing regression methods, and avoiding common pitfalls in Azure solutions.
Table of Contents
Question
Which of the following is a type of unsupervised machine learning that trains a model without using previously known label values?
A. Polynomial regression
B. Linear regression
C. Clustering
D. Logistic regression
Answer
C. Clustering
Explanation
Clustering is an unsupervised learning technique that groups similar data points together based on their features, without any predefined labels. The algorithm analyzes the data and identifies patterns or relationships between features to automatically categorize data points into clusters. These clusters represent groups of data points that share similar characteristics. Examples of clustering applications include:
- Grouping customers based on their purchase history and demographics for targeted marketing campaigns.
- Identifying anomalies in sensor data that deviate from typical patterns.
- Segmenting images based on their content, such as grouping pictures of cats together.
Logistic, linear, and polynomial regression are all supervised learning techniques. They require labeled data for training, meaning that the data points have associated labels or target values that the model learns to predict based on their features. For example, in predicting house prices, the labels would be the actual prices, and the features would be size, location, etc.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.