Autoplay
Autocomplete
Speed
Previous Lecture
Complete and Continue
Master Clustering Analysis for Data Science Using MATLAB!
Welcome to the Course!
Introduction to the course (4:07)
K-Means Clustering
K-Means intuition (12:18)
Choosing the right number of clusters (15:35)
K-Means in MATLAB (part 1) (21:15)
K-Means in MATLAB (part 2) (12:57)
K-Means limitations - (part 1 - clusters with different sizes) (10:30)
K-Means limitations - (part 2 - clusters with non spherical shapes) (9:33)
K-Means limitations - (part 3 - clusters with varying densities) (5:33)
K-Means clustering code and data
Mean Shift Clustering
Intuition of mean shift (9:23)
Mean shift in Python (10:46)
Mean shift performance in cases where k-mean fails (part 1) (7:17)
Mean shift performance in cases where k-mean fails (part 2) (12:21)
Mean shift code and data
DBSCAN Clustering
Intuition of DBSCAN (9:21)
DBSCAN in MATLAB (14:39)
DBSCAN on clusters with varying sizes (7:03)
DBSCAN on clusters with different shapes and densities (10:57)
DBSCAN for handling noise (7:14)
Practical activity
DBSCAN code and data
Hierarchical Clustering
Hierarchical clustering intuition (part 1) (9:50)
Hierarchical clustering intuition (part 2) (15:47)
Hierarchical clustering in MATLAB (12:21)
Hierarchical clustering code and data
Applications of Clustering
Image compression (part 1) (12:43)
Image compression (part 2) (7:29)
Clustering sentences (part 1) (14:08)
Clustering sentences (part 2) (11:02)
Applications code and data
DBSCAN on clusters with different shapes and densities
Lecture content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock