ML models from Scratch - K-Means Clustering Implementation from Scratch with Python
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Learn how to implement K-Means Clustering from scratch using Python in this comprehensive tutorial. K-Means Clustering is a popular unsupervised machine learning algorithm used for data clustering and segmentation.
In this video, we'll dive into the mathematics behind K-Means Clustering and implement it from scratch using Python. You'll learn how to write the K-Means algorithm from the ground up, including data preprocessing, centroid initialization, and cluster assignment.
By the end of this video, you'll have a solid understanding of how K-Means Clustering works and be able to apply it to your own machine learning projects. Whether you're a beginner or an experienced data scientist, this video is perfect for anyone looking to improve their skills in machine learning and Python programming.
🎯 Topics covered in this video:
⌨️ K-Means Algorithm : Steps
⌨️ K-Means Implementation with Python - K, Centroid initialization, cluster assignment, updating centroids, convergence.
⌨️ Plotting the clusters along with Centroids.
Resources:
🔗 Code : github.com/mohangollapalli/ml_models/tree/bec3e2f7b74eeecf2dfcab0a3855269fac33a205/KMeans
💡 Why This Video?
This tutorial is designed to help you not only code the model but also grasp the underlying concepts that power K-Means Clustering algorithm. Whether you're prepping for a data science interview or looking to build your own projects, mastering Clustering algorithms will set a strong foundation for more advanced machine learning techniques.
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Time breaks:
00:00 Intro. & K-Means algorithm Steps
00:49 Centroid Initialization : Random Initialization.
01:37 Cluster label assignment, recompute centroids, convergence.
04:28 Dataset and data pre-processing.
06:35 Implementing Step 1, 2: Choosing K, centroid initialization.
08:05 Implementing Step 3: Assign cluster labels
14:26 Implementing Step 4, 5 and Convergence
18:51 Plotting the clusters with centroids
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