Discover why machine learning excels at analyzing unstructured data compared to traditional programming. Get a clear, expert answer for the IBM AI Fundamentals certification exam.
Table of Contents
Question
Which of the following types of data can be analyzed more quickly by machine learning than by a programmable computer?
A. Unstructured
B. Structured
C. Systematic
D. Organized
Answer
A. Unstructured
Explanation
Machine learning can analyze unstructured data more quickly than a programmable computer because machine learning doesn’t rely on programming instructions to work with unstructured data.
Machine learning is particularly well-suited for analyzing unstructured data more quickly and effectively than traditional programmable computers.
Unstructured data refers to information that doesn’t fit neatly into predefined data models or schemas. Examples include images, audio, video, and free-form text. This type of data is often more complex, variable, and voluminous compared to structured data like database records.
With unstructured data, it’s difficult to manually define rules and logic to analyze it via traditional programming. Machine learning, in contrast, automatically identifies patterns, features, and relationships in the data by learning from examples. ML models can be trained on large unstructured datasets to classify, cluster, extract insights, and make predictions.
Some key advantages of machine learning over standard programming for unstructured data analysis include:
- Adaptability: ML models learn and improve with more data exposure, adapting to new patterns.
- Scalability: ML can process huge volumes of unstructured data efficiently.
- Complexity: ML can detect subtle, non-linear, high-dimensional patterns in unstructured data.
- Generalization: Well-trained ML models can generalize insights to new, unseen data.
So in summary, while programmable computers rely on explicitly defined instructions, machine learning autonomously learns from unstructured data – enabling it to analyze this complex data type much more quickly and capably. The other options (structured, systematic, organized data) are better suited for traditional programming approaches.
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