Feb 23 · 13 min read · This is Week 19 of my data science journey, the week I stopped telling the algorithm what to find and started letting it discover patterns on its own. If you've been following since Week 18, welcome b
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Feb 8 · 6 min read · 🧭 Table of Contents 🔍 Introduction to Clustering 🧠 What is K-Means Clustering? 💡 Why do we use K-Means? 📌 Key Concepts of K-Means 🎯 Objective of K-Means ⚙️ Step-by-Step Working of K-Means 🧪 Hands-on Example (Manual Calculation – 2D Data...
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Dec 3, 2025 · 13 min read · 1. Introduction In the past few months, we’ve heard consistent feedback from users and partners: while our goal of providing a scalable, high-performance alternative to pgvector is well-received, index build time and memory usage remain major concern...
Join discussionOct 15, 2025 · 2 min read · 📖 Clustering models are unsupervised learning algorithms that group similar data points together without needing labelled data.They’re widely used in market segmentation, anomaly detection, image analysis, and recommendation systems — helping AI unc...
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Aug 11, 2025 · 6 min read · K-Means is one of the simplest and most popular clustering algorithms.It finds groups in your data, and every point belongs to one of these groups (called clusters). Before we dive into K-Means, let's first set the stage. In supervised learning, you ...
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Aug 6, 2025 · 4 min read · Imagine walking into a party... You don’t know anyone. There are no name tags, No signs,No seating charts but somehow, you start noticing patterns: That group by the buffet is talking about tech. The ones near the speaker? All dancing. A bunch by the...
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Aug 1, 2025 · 2 min read · In this article, I explored a small project that introduced the basic concept of unsupervised learning, specifically focusing on K-Means Clustering. The main idea behind clustering is to divide a dataset into distinct groups or clusters, where each d...
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