Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Master Clustering Analysis for Data Science using Python
Introduction to the Course
Introduction (4:22)
KMeans Clustering
Code and Data
1 - KMeans intuition (12:18)
2 - Choosing the right number of clusters (15:35)
3 - KMeans in Python (Part 1 (18:35)
4 - KMeans in Python (Part 2) (9:41)
5 - KMeans Limitations - (Part 1-Clusters with different sizes) (10:30)
6 - KMeans Limitations - (Part-2-Clusters with non spherical shapes) (10:37)
7 - KMeans Limitations - (Part 3-Clusters with varying densities) (5:22)
Mean Shift Clustering
Code and Data
1 - Intuition of Mean Shift (9:23)
2 - Mean Shift in Python (9:23)
3 - Mean Shift Performance in Cases where Kmean Fails (Part 1) (8:51)
4 - Mean Shift Performance in Cases where Kmean Fails (Part 2) (11:34)
DBSCAN Clustering
Code and Data
1 - Intuition of DBSCAN (9:21)
2 - DBSCAN in python (12:47)
3 - DBSCAN on clusters with varying sizes (6:29)
4 - DBSCAN on clusters with different shapes and densities (11:27)
5 - DBSCAN for handling noise (8:00)
6 - Practical Activity
Hierarchical Clustering
Code and Data
1 - Hierarchical Clustering Intuition (Part 1) (9:50)
2 - Hierarchical Clustering Intuition (Part 2) (15:47)
Hierachical Clustering in python (11:27)
HDBSCAN Clustering
Code and Data
1 - HDBSCAN Intuition (18:53)
2 - HDBSCAN in Python (9:41)
3 - HDBSCAN clustering on different sizes, shapes and densities (6:58)
4 - HDBSCAN for handling noise (13:31)
Application of Clustering
Code and Data
1 - Image Compression (Part 1) (11:37)
2 - Image Compression (Part 2) (10:55)
3 - Clustering Sentences (Part 1) (11:23)
4 - Clustering sentences (Part 2) (8:48)
3 - KMeans in Python (Part 1
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock