19. Clustering with K Means Algorithm | Hands on Example | Machine learning

In this video, we explore Clustering using the popular K-Means Algorithm, a powerful unsupervised learning technique in Machine Learning. K-Means is widely used for grouping similar data points into clusters based on their features.

We’ll walk through the algorithm’s steps and demonstrate how it works using a hands-on example in Python. By the end of this video, you’ll have a clear understanding of how to implement K-Means clustering and evaluate its results.

What You'll Learn:
Introduction to Clustering: Understand the concept of clustering and why it’s an important technique in unsupervised learning.
What is K-Means?: Learn about the K-Means algorithm, its steps, and how it partitions data into clusters.
Choosing the Right K (Number of Clusters): Learn how to determine the optimal number of clusters using methods like the Elbow Method.
Hands-on Example: A step-by-step Python tutorial using the scikit-learn library to apply K-Means clustering to a real-world dataset.
Visualizing Results: How to visualize and interpret the clustering results with matplotlib.
Evaluating the Model: Understand how to evaluate the performance of your clustering model.
Why K-Means?
K-Means is widely used due to its simplicity and efficiency in handling large datasets. It’s ideal for tasks like customer segmentation, image compression, and anomaly detection.
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Видео 19. Clustering with K Means Algorithm | Hands on Example | Machine learning канала Professor Rahul Jain
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