In this repository, you will find code snippets and practical examples aimed at building proficiency in various unsupervised machine learning techniques. These resources are designed to help users grasp core concepts and apply them to real-world data. The topics covered include:
- Clustering: Techniques such as K-Means, Gaussian Mixture Models (GMMs), DBSCAN, and more.
- Dimensionality Reduction: Methods like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for simplifying complex data.
- Association Rules: Understanding relationships between variables within large datasets.
- Auto-Encoders: Implementing neural network-based models for data compression and anomaly detection.
All code is written in Python and organised into multiple Jupyter Notebooks for easy exploration and execution. The repository also includes a 'data' folder containing various datasets to facilitate hands-on learning as you run the provided notebooks.