Welcome to our collection of unsupervised learning tutorials! Unsupervised learning is a type of machine learning where the algorithm is given a dataset without any labels or target outputs. The goal of unsupervised learning is to find patterns, relationships, and insights within the data.
Common Unsupervised Learning Techniques
- Clustering: Grouping similar data points together.
- K-Means Clustering: A popular method for clustering data.
- Hierarchical Clustering: A method that builds a hierarchy of clusters.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining as much information as possible.
- Principal Component Analysis (PCA): A technique used for dimensionality reduction.
- t-SNE: A technique used for visualizing high-dimensional data.
- Association Rules: Finding interesting relationships between variables in large databases.
- Apriori Algorithm: A classic algorithm used for finding association rules.
Tutorial Resources
For more in-depth tutorials on unsupervised learning, check out our Machine Learning Basics section.
Clustering Visualization
Conclusion
Unsupervised learning is a powerful tool for exploring and understanding data. By applying these techniques, you can uncover hidden patterns and insights that can be valuable for decision-making and further analysis.
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