Machine Learning
1,521 learners
Unsupervised Learning and Clustering
Navigate through the intricacies of Unsupervised Learning and Clustering in this hands-on course. Skip the high-level libraries and build core aspects of unsupervised learning methods from scratch, including k-Means, mini-batch k-Means, Principal Component Analysis, and DBSCAN. Learn to assess cluster quality with crucial clustering metrics like homogeneity, completeness, and v-metric.
Numpy
Python
4 lessons
15 practices
3 hours
Machine Learning Model Development
Lessons and practices
Visualize Clustering with k-Means Algorithm
Exploring Space with More Clusters
Calculating the New Center in Clustering
Implementing the k-Means Centroid Update
Visualizing Mini-Batch K-Means Clustering
Adjusting Batch Size in Mini-Batch K-Means
Updating the Mini-Batch K-Means Centroids
Update Cluster Centers in Mini-Batch K-Means
Visualizing Dimension Reduction with PCA
Expanding the Horizon with Two Principal Components
Unveiling the Secrets of PCA: Eigendecomposition and Transformation
DBSCAN Clustering Visualization
Adjusting DBSCAN Epsilon Value
Navigating Through the Stars: Adding DBSCAN Logic
Mapping and Queueing in DBSCAN Clustering
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