6. What is Data and Dataset in Machine Learning?

Data refers to individual pieces of information that can be collected and analyzed. This can include various types of information such as numbers, text, images, or audio. Data can be raw and unstructured or processed and organized.

Dataset is a structured collection of data, typically organized in a table or matrix format where rows represent individual data points (instances) and columns represent features (attributes or variables). Datasets are used to train and evaluate machine learning models, and they can be labeled or unlabeled depending on whether they include target outputs.

7. What is Quantitative Data?

Quantitative data refers to information that can be measured and expressed numerically. It often involves counts or measurements and can be analyzed using statistical methods. This type of data can be divided into two main categories:

Discrete Data: This consists of countable values, such as the number of students in a classroom or the number of cars in a parking lot.

Continuous Data: This includes measurable quantities that can take any value within a given range, such as height, weight, or temperature.

8. What is Qualitative Data?

Qualitative data refers to non-numeric information that describes qualities or characteristics. This type of data is often used to capture complex concepts, opinions, and experiences. It can be categorized into different types, including:

Nominal Data: This represents categories without a specific order, such as colors (red, blue, green) or types of cuisine (Italian, Mexican, Chinese).

Ordinal Data: This involves categories with a meaningful order, such as ratings (like “satisfied,” “neutral,” “dissatisfied”) or class rankings(like low, medium, high).

9. What is Labeled Data in Machine Learning?

Labeled data in machine learning refers to datasets that have been annotated with labels or tags that identify the correct output for each input example. Each data point in a labeled dataset consists of features (input) and a corresponding label (output).

For example:
An image of a cat might be labeled as “cat”.
An image of a dog might be labeled as “dog”.
A review could be labeled as “positive”, “negative”, or “neutral”.

10. What is Unlabeled Data in Machine Learning?

Unlabeled data in machine learning refers to datasets that do not have associated labels or annotations indicating the correct output for each input example. This type of data consists solely of input features without any corresponding target values. Unlabeled data is common in real-world scenarios where labeling can be expensive, time-consuming, or impractical.

For example:
Text documents (e.g., articles, blogs)
Images (e.g., photographs, videos)
Sensor data (e.g., temperature, humidity readings)
Web traffic logs (e.g., user interactions, clickstreams)

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