Master Clustering Analysis for Data Science using Python

Learn to implement clustering algorithms using Python with practical examples and datasets.

Course curriculum

1. Introduction
1. KMeans intuition
2. Choosing the right number of clusters
3. KMeans in Python (Part 1
4. KMeans in Python (Part 2)
5. KMeans Limitations - (Part 1-Clusters with different sizes)
6. KMeans Limitations - (Part-2-Clusters with non spherical shapes)
7. KMeans Limitations - (Part 3-Clusters with varying densities)
1. Intuition of Mean Shift
2. Mean Shift in Python
3. Mean Shift Performance in Cases where Kmean Fails (Part 1)
4. Mean Shift Performance in Cases where Kmean Fails (Part 2)
1. Intuition of DBSCAN
2. DBSCAN in Python
3. DBSCAN on clusters with varying sizes
4. DBSCAN on clusters with different shapes and densities
5. DBSCAN for handling noise
6. Practical Activity
1. Hierarchical Clustering Intuition (Part 1)
2. Hierarchical Clustering Intuition (Part 2)
3. Hierarchical Clustering in Python
1. HDBSCAN Intuition
2. HDBSCAN in Python
3. HDBSCAN clustering on different sizes, shapes and densities
4. HDBSCAN for handling noise
1. Image Compression (Part 1)
2. Image Compression (Part 2)
3. Clustering Sentences (Part 1)
4. Clustering sentences (Part 2)

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