## Questions and Answers

Explain the difference between supervised and unsupervised learning in machine learning.

Supervised learning involves training a model on a labeled dataset, where the model learns to make predictions based on input-output pairs. Unsupervised learning, on the other hand, involves training a model on an unlabeled dataset, where the model learns to find patterns or structures in the data without specific input-output pairs.

What is the formula for Euclidean distance between two points with coordinates $(x_1, y_1)$ and $(x_2, y_2)$? How is Euclidean distance used in K-means clustering?

The formula for Euclidean distance between two points with coordinates $(x_1, y_1)$ and $(x_2, y_2)$ is $d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$. Euclidean distance is used in K-means clustering to measure the distance between data points and cluster centroids, helping to assign each data point to the nearest centroid for clustering.

What is the role of feature reduction in machine learning? Provide an example of a model used for feature reduction.

Feature reduction in machine learning aims to reduce the dimensionality of the input data by selecting a subset of relevant features, which can improve model performance and reduce computational complexity. An example of a model used for feature reduction is Principal Component Analysis (PCA).

Define clustering in the context of machine learning and mention different types of clustering.

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How is data preprocessing useful in machine learning?

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## Study Notes

### Supervised and Unsupervised Learning

- Supervised learning involves training a model on labeled data to make predictions on new, unseen data.
- Unsupervised learning involves training a model on unlabeled data to discover patterns or relationships.

### Euclidean Distance and K-means Clustering

- The formula for Euclidean distance between two points $(x_1, y_1)$ and $(x_2, y_2)$ is: $\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$
- Euclidean distance is used in K-means clustering to measure the distance between data points and centroids.
- K-means clustering is a type of unsupervised learning algorithm that groups similar data points into clusters based on their features.

### Feature Reduction

- Feature reduction is the process of reducing the number of features in a dataset to improve model performance and reduce dimensionality.
- An example of a model used for feature reduction is Principal Component Analysis (PCA).
- PCA reduces dimensionality by projecting high-dimensional data onto a lower-dimensional space using orthogonal transformation.

### Clustering

- Clustering is the process of grouping similar data points into clusters based on their features and characteristics.
- Types of clustering include: Hierarchical Clustering, K-means Clustering, and Density-Based Clustering.

### Data Preprocessing

- Data preprocessing is the process of cleaning, transforming, and preparing data for model training.
- Data preprocessing is useful in machine learning because it improves data quality, reduces noise, and increases model accuracy.

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## Description

Test your knowledge of machine learning using Python with this question bank for the semester exam. Covering topics such as the importance of machine learning, supervised and unsupervised learning, model evaluation in regression, feature reduction, and more, this quiz will help you prepare for the November 2023 exam.