Microsoft Azure AI Fundamentals AI-900 Practice Question
An AI engineer is working on a project that involves analyzing vast amounts of unstructured data, such as images and speech. She needs to build a model that can automatically learn hierarchical representations from raw data without extensive feature engineering.
Which machine learning technique is most appropriate for this scenario?
Deep Learning - This is the correct answer. Deep learning is the most appropriate technique for analyzing unstructured data like images and speech, as it can automatically learn hierarchical representations from raw data. Deep learning models, especially neural networks, excel at processing and extracting meaningful features from complex data without requiring extensive manual feature engineering.
Clustering Algorithms are used for grouping data into clusters based on similarity, but they are not designed to automatically learn hierarchical representations from raw unstructured data like images and speech.
Regression Algorithms are used for predicting continuous numerical outcomes based on input features, but they are not suited for learning hierarchical representations from unstructured data.
Decision Trees are used for classification or regression tasks by splitting data into decision rules based on features. They require predefined features and are less effective than deep learning for automatically learning hierarchical representations from raw, unstructured data like images and speech.
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Microsoft Azure AI Fundamentals AI-900
Describe Fundamental Principles of Machine Learning on Azure
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