Customer Segmentation and Clustering with Python 5 Sections20 LessonsLifetime Access
Curriculum
- 5 Sections
- 20 Lessons
- Lifetime Access
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- K-Means Clustering10 Items
- 1.1Understanding Clustering in Machine Learning: Introduction to Unsupervised Learning
- 1.2Visualizing the Machine Learning Algorithm: K-Means Clustering Tutorial
- 1.3Using the Elbow Method in K-Means Clustering: A Step-by-Step Guide
- 1.4Solving the Random Initialization Trap in Clustering with the K-Means++ Algorithm
- 1.5Preparing Data in Google Colab/Jupyter for K-Means Clustering
- 1.6Implementing the Elbow Method for K-Means Clustering in Python
- 1.7Optimizing K-Means Clustering: WCSS and Elbow Method Implementation
- 1.8Plotting the Elbow Method Graph for K-Means Clustering in Python
- 1.9Visualizing K-Means Clusters of Customer Data with Python Scatter
- 1.10Plotting Customer Segments in Python: Visualizing K-Means Clusters
- Hierarchical Clustering7 Items
- 2.1Performing Hierarchical Clustering: A Step-by-Step Guide for Machine Learning
- 2.2Visualizing Cluster Dissimilarity with Dendrograms in Hierarchical Clustering
- 2.3Mastering Hierarchical Clustering with Dendrogram Analysis and Threshold Setting
- 2.4Implementing Hierarchical Clustering: Building a Dendrogram with SciPy
- 2.5Visualizing Hierarchical Clustering: Dendrogram Basics in Python
- 2.6Interpreting Dendrograms: Optimal Clusters in Hierarchical Clustering
- 2.7Building a Hierarchical Clustering Model with Scikit-Learn in Python
- Resources1 Item
- Practice Lab1 Item
- Claim Your Certificate1 Item
Customer Segmentation and Clustering with Python Practice Lab
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