In this course, you will :
- Describe the types of problems that are suitable for Unsupervised Learning approaches.
- Explain the curse of dimensionality and how it makes clustering with many features difficult.
- Explain and apply common clustering and dimension-reduction algorithms.
- When appropriate, try clustering points and compare the performance of per-cluster models.
- Understand metrics relevant for characterising clusters.
1. Introduction to Unsupervised Learning and K Means
- Introduction to Unsupervised Learning
- Introduction to Clustering
- K Means Notebook
2. Selecting a clustering algorithm
- Distance Metrics
- Curse of Dimensionality Notebook
- Hierarchical Agglomerative Clustering
- Mean Shift
- Comparing Algorithms
- Clustering Notebook
3. Dimensionality Reduction
- Dimensionality Reduction
- PCA Notebook
- Dimensionality Reduction Notebook
- Non Negative Matrix Factorization
- Non Negative Matrix Factorization Notebook
- Dimensionality Reduction Imaging Example