Cluster Analysis and Unsupervised Machine Learning in Python
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
4.64 (5220 reviews)
30 550
students
8 hours
content
May 2025
last update
$29.99
regular price
What you will 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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Our Verdict
This course stands out with its strong emphasis on the mathematical foundations of cluster analysis and unsupervised machine learning techniques. While some may find the theoretical proofs excessively detailed, others will appreciate the rigor and depth provided by the course. The implementation of two algorithms from scratch is a valuable exercise in understanding these techniques. However, the course could benefit from more real-world examples, practical assignments and a better balance between theory and practice for non-mathematicians.
What We Liked
- Comprehensive coverage of cluster analysis and unsupervised machine learning techniques
- In-depth exploration of the mathematics behind each algorithm
- Implementation of two out of three algorithms from scratch
- Clear explanation of Gaussian Mixture Models and its application
Potential Drawbacks
- Some theoretical proofs may seem unnecessary for some learners
- Lectures might feel a bit disorganized and lack real-world examples
- Absence of guided assignments or capstone projects to apply learning
- Overemphasis on mathematical concepts could be challenging for non-mathematicians
Related Topics
825684
udemy ID
19/04/2016
course created date
21/09/2019
course indexed date
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