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Questions and Answers
Which of the following is an example of a low-volume database?
Which of the following is an example of a low-volume database?
- Analytic Sandbox
- Statistical approach
- Isolated Data Marts
- Data Warehouses (correct)
What are spreadsheets and low-volume databases examples of?
What are spreadsheets and low-volume databases examples of?
- Isolated Data Marts (correct)
- Data Warehouses
- Analytic Sandbox
- Statistical approach
Which term describes the use of spreadsheets and low-volume databases?
Which term describes the use of spreadsheets and low-volume databases?
- Statistical approach
- Data Warehouses
- Analytic Sandbox (correct)
- Isolated Data Marts
Which type of problem does 'unsupervised' refer to in machine learning?
Which type of problem does 'unsupervised' refer to in machine learning?
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What does 'unsupervised' aim to find within the data?
What does 'unsupervised' aim to find within the data?
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What is the main characteristic of the data used in 'unsupervised' learning?
What is the main characteristic of the data used in 'unsupervised' learning?
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Study Notes
Low-Volume Databases
- Spreadsheets are considered a type of low-volume database.
- Examples of low-volume databases include Microsoft Excel and Google Sheets.
Usage of Spreadsheets and Low-Volume Databases
- Spreadsheets and low-volume databases are often used for data storage, organization, and analysis in various applications.
Term Describing Spreadsheets and Low-Volume Databases
- The term "personal databases" describes the use of spreadsheets and low-volume databases, emphasizing their suitability for individual or small-scale data tasks.
Unsupervised Learning in Machine Learning
- 'Unsupervised' learning refers to a type of problem in machine learning where the model is trained without labeled data or specific outcomes.
Goals of Unsupervised Learning
- The primary aim of unsupervised learning is to identify patterns or structures within the data itself without prior knowledge of categories or labels.
Characteristics of Data in Unsupervised Learning
- Data used in unsupervised learning lacks predefined labels or classifications, allowing the algorithm to explore natural groupings and distributions.
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