Welcome to our tutorial on Unsupervised Learning! This page will guide you through the basics of unsupervised learning, its applications, and how it differs from supervised learning.
What is Unsupervised Learning?
Unsupervised learning is a type of machine learning where the algorithm is given a dataset without any labels or target outputs. The goal of the algorithm is to find patterns and structures in the data.
Key Points of Unsupervised Learning:
- No labeled data: Unlike supervised learning, unsupervised learning doesn't require labeled data.
- Pattern recognition: Algorithms try to find patterns and structures in the data.
- Applications: Clustering, association, dimensionality reduction.
Types of Unsupervised Learning
There are several types of unsupervised learning algorithms, each with its own strengths and applications.
Clustering
Clustering is the process of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
- K-Means: A simple and popular clustering algorithm.
- Hierarchical clustering: A method of creating a hierarchy of clusters.
Association
Association rules are used to discover interesting relationships between variables in large databases.
- Apriori: An algorithm used for finding frequent itemsets in transaction databases.
- Eclat: A method for finding frequent itemsets.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
- PCA (Principal Component Analysis): A technique used to reduce the dimensionality of large datasets.
- t-SNE: A non-linear dimensionality reduction technique.
Applications of Unsupervised Learning
Unsupervised learning has various applications in different fields, such as:
- Market basket analysis: Discovering patterns in customer purchasing behavior.
- Anomaly detection: Identifying unusual patterns that may indicate an attack or fraud.
- Image segmentation: Grouping pixels in an image based on their similarity.
Further Reading
If you're interested in learning more about unsupervised learning, we recommend checking out the following resources:
By understanding the basics of unsupervised learning, you'll be better equipped to tackle real-world problems and make data-driven decisions. Happy learning!