11h ago · 16 min read · 1. INTRODUCTION Data science is not just about building models. It is about understanding patterns hidden beneath layers of raw, unstructured, and high-dimensional data. In my Week 19 internship at Da
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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 12 · 11 min read · Why Traditional Redis Deployments Fail at Scale Single-instance Redis with manual failover scripts was acceptable when applications served regional traffic with predictable load patterns. That model collapses under modern constraints. A standalone Re...
Join discussionFeb 3 · 7 min read · Node.js is widely known for its non-blocking I/O model and event-driven architecture. It excels at handling thousands of concurrent connections with minimal overhead. But there’s a catch. By default, a Node.js application runs on a single CPU core. E...
Join discussionFeb 2 · 6 min read · Node.js is widely celebrated for its non-blocking I/O model and event-driven architecture. It excels at handling thousands of concurrent connections with minimal overhead. But there’s a catch. By default, a Node.js application runs on a single CPU co...
Join discussionJan 11 · 5 min read · In this article of our Unsupervised Learning series, we explore another important clustering algorithm that comes right after K-Means in popularity and usefulness — DBSCAN. While K-Means works very well for many problems, it assumes that clusters are...
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Jan 9 · 4 min read · This is another article in our Unsupervised Learning series. Today, we’ll explore one of the most popular and intuitive algorithms used in unsupervised learning — K-Means Clustering. Before jumping into K-Means, let’s quickly recall what unsupervised...
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Dec 18, 2025 · 7 min read · Exam weight: 15–20% Study roadmap for this topic: ML models: features and labels, training, and validation ML scenarios: regression, classification, and clustering ML capabilities: automation, data/compute services, model management, and deploymen...
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