Cluster Analysis and Unsupervised Machine Learning in Python Course
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own and learn things just by looking for patterns.
Do you ever wonder how we get the data that we use in our supervised machine learning algorithms? We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys. If you haven’t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!
Those “Y” s have to come from somewhere, and a lot of the time that involves manual labor. Sometimes, you don’t have access to this kind of information, or it is infeasible or costly to acquire. But you still want to have some idea of the structure of the data. If you’re doing data analytics automating pattern recognition in your data would be invaluable.
What you’ll learn
- Understand the regular K-Means algorithm.
- Understand and enumerate the disadvantages of K-Means Clustering
- Understand the soft or fuzzy K-Means Clustering algorithm.
- Implement Soft K-Means Clustering in Code
- Understand Hierarchical Clustering
- Explain algorithmically how Hierarchical Agglomerative Clustering works.
- Apply Scipy’s Hierarchical Clustering library to data.
- Understand how to read a dendrogram.
- Understand the different distance metrics used in clustering.
- Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA.
- Understand the Gaussian mixture model and how to use it for density estimation.
- Write a GMM in Python code.
- Explain when GMM is equivalent to K-Means Clustering
- Explain the expectation-maximization algorithm.
- Understand how GMM overcomes some disadvantages of K-Means
- Understand the Singular Covariance problem and how to fix it.
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