Cluster Analysis

Kurssisisältö

Cluster Analysis

Cluster Analysis

1. Clustering Fundamentals

Introduction to Clustering Clustering Vs Classification Clustering Algorithms and Libraries

2. Core Concepts

Missing Values Handling Categorical Features Encoding Data Normalization Distance Measures Linkages Challenge: Preprocessing the Dataset

3. K-Means

What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSS Finding Optimal Number of Clusters Using Silhouette Score Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing K-Means Clustering

4. Hierarchical Clustering

How Hierarchical Clustering Works?Optimal Number of Clusters Implementing on Dummy Dataset Implementing on Customers Dataset Challenge: Implementing Hierarchical Clustering

5. DBSCAN

Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing DBSCAN

6. GMMs

Problem Statement What is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy Data Implementing GMM on Real Data Challenge: Implementing Gaussian Mixture Models Conclusion

Clustering Vs Classification

Clustering and classification are different machine learning techniques with distinct goals.

Classification is about sorting into known categories (like sorting mail into pre-labeled boxes). Clustering, on the other hand, is about discovering categories (like finding groups in unsorted mail).

Classification is commonly used in spam detection or image recognition, where the categories are predefined. On the other hand, clustering is used in scenarios like customer segmentation or discovering topics in a collection of documents, where the goal is to reveal hidden patterns or groupings.

In short, classification is about predicting known categories, while clustering helps discover unknown groupings. The choice between the two depends on the nature of your data and the problem you're trying to solve.

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Kurssisisältö

Cluster Analysis

Cluster Analysis

1. Clustering Fundamentals

Introduction to Clustering Clustering Vs Classification Clustering Algorithms and Libraries

2. Core Concepts

Missing Values Handling Categorical Features Encoding Data Normalization Distance Measures Linkages Challenge: Preprocessing the Dataset

3. K-Means

What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSS Finding Optimal Number of Clusters Using Silhouette Score Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing K-Means Clustering

4. Hierarchical Clustering

How Hierarchical Clustering Works?Optimal Number of Clusters Implementing on Dummy Dataset Implementing on Customers Dataset Challenge: Implementing Hierarchical Clustering

5. DBSCAN

Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing DBSCAN

6. GMMs

Problem Statement What is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy Data Implementing GMM on Real Data Challenge: Implementing Gaussian Mixture Models Conclusion

Clustering Vs Classification

Clustering and classification are different machine learning techniques with distinct goals.

Classification is about sorting into known categories (like sorting mail into pre-labeled boxes). Clustering, on the other hand, is about discovering categories (like finding groups in unsorted mail).

Classification is commonly used in spam detection or image recognition, where the categories are predefined. On the other hand, clustering is used in scenarios like customer segmentation or discovering topics in a collection of documents, where the goal is to reveal hidden patterns or groupings.

In short, classification is about predicting known categories, while clustering helps discover unknown groupings. The choice between the two depends on the nature of your data and the problem you're trying to solve.

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Kiitos palautteestasi!

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