Building Clustering Models with scikit-learn

سرفصل های دوره

This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift and DBScan clustering algorithms, as well as the role of hyperparameter tuning, and performing clustering on image data.

1. Course Overview

1. Course Overview

2. Building a Simple Clustering Model in scikit-learn

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Supervised and Unsupervised Learning

05. Clustering Objectives and Use Cases

06. K-means Clustering

07. Evaluating Clustering Models

08. Getting Started with scikit-learn Install and Setup

09. Performing K-means Clustering

10. Evaluating K-means Clustering

11. Exploring the Iris Dataset

12. Performing K-means Clustering and Evaluation

3. Performing Clustering Using Multiple Techniques

01. Module Overview

02. Categories of Clustering Algorithms

03. Setting up Helper Functions to Perform Clustering

04. Choosing Clustering Algorithms

05. Hierarchical Clustering

06. Agglomerative Clustering

07. DBSCAN Clustering

08. Mean-shift Clustering

09. BIRCH Clustering

10. Affinilty Propagation Clustering

11. Mini-batch K-means Clustering

12. Spectral Clustering Using a Precomputed Matrix

4. Hyperparameter Tuning for Clustering Models

1. Module Overview

2. Understanding the Silhouette Score

3. K-means Number of Clusters- The Elbow Method

4. K-means Number of Clusters- The Silhouette Method

5. Seeds and Distance Measures

6. Hyperparameter Tuning- K-means Clustering

7. Hyperparameter Tuning- DBSCAN Clustering

8. Hyperparameter Tuning- Mean-shift Clustering

5. Applying Clustering to Image Data

1. Module Overview

2. Images as Matrices

3. Exploring the MNIST Handwritten Digits Dataset

4. Clustering Image Data

5. Summary and Further Study

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تولید کننده: PluralSight