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Clustering Demystified: K-Means, Hierarchical, and DBSCAN Explained with Real-World Intuition

May 21 · 16 min read · If you've ever looked at a messy spreadsheet of customer data and thought, "I wish I could find hidden patterns in here without having to label anything" — you're already thinking about clustering. Cl

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DKDivyajot Kaurai-beginners-journey.hashnode.dev

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Part 3: Types of Machine Learning

Apr 22 · 4 min read · Think about how you learned things growing up-being taught by someone, figuring things out on your own, or learning from mistakes. Machine Learning follows these same patterns In this blog, we'll brea

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SShrutiskeptical-machine-learning.hashnode.dev

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How Context Shapes Learning

Apr 19 · 4 min read · In our last article, we looked at the Learning of Machine. We saw how Mean Squared Error \((MSE)\) acts as a measure of regret and Gradient Descent acts as a compass to minimize that regret. But even

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VUVishal Uttam Manevishal-uttam-mane-super-unsuper-learning.hashnode.dev

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Supervised vs Unsupervised Learning in Real Applications

Apr 14 · 4 min read · Machine learning systems are broadly categorized into supervised and unsupervised learning paradigms, each addressing fundamentally different problem spaces. While the theoretical distinction is strai

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RSRahul Sehrawatai-zero-to-hero.hashnode.dev

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Unsupervised Learning, or Why Patterns Appear on Their Own

Apr 12 · 10 min read · You walk into a party where you don't know anyone. For the first ten minutes, it's just a blur of faces. But by the end of the first hour, without anyone explaining anything to you, your brain has quietly sorted the room. There's the loud cluster nea...

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STSami Thakursamithakur.hashnode.dev

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Machine Learning Introduction: A Beginner’s Guide

Apr 2 · 2 min read · Machine Learning algorithms does not rely on humans to which part to choose they derive it based on data provided to them It is a field of Artificial Intelligence where computers learn patterns from d

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BSBerkan Sesensesenai.hashnode.dev

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Hidden Markov Models: When Clusters Have Memory

Mar 31 · 15 min read · In the previous post, we clustered data into groups with K-Means and GMMs. Both methods assume observations are independent — each data point is analysed in isolation, with no regard for what came bef

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BSBerkan Sesensesenai.hashnode.dev

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From K-Means to GMM: Hard vs Soft Clustering

Mar 30 · 14 min read · You have a pile of unlabelled data and you want to find groups in it. K-Means is the algorithm everyone reaches for first — it's fast, simple, and usually works. But it makes a bold assumption: every

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BSBerkan Sesensesenai.hashnode.dev

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Gaussian Mixture Models: The EM Algorithm in Practice

Mar 28 · 13 min read · You have a dataset that clearly contains distinct groups, but nobody has labelled them. K-Means would give you hard boundaries — each point belongs to exactly one cluster. But what if a data point sit

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BSBerkan Sesensesenai.hashnode.dev

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The EM Algorithm: An Intuitive Guide with the Coin Toss Example

Mar 27 · 11 min read · Imagine you're a casino inspector. You suspect a dealer has been switching between two biased coins, but you only have records of the outcomes - not which coin was used for each game. How do you figur

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#unsupervised-learning

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#unsupervised-learning

Trending tags this week

Clustering Demystified: K-Means, Hierarchical, and DBSCAN Explained with Real-World Intuition

Part 3: Types of Machine Learning

How Context Shapes Learning

Supervised vs Unsupervised Learning in Real Applications

Unsupervised Learning, or Why Patterns Appear on Their Own

Machine Learning Introduction: A Beginner’s Guide

Hidden Markov Models: When Clusters Have Memory

From K-Means to GMM: Hard vs Soft Clustering

Gaussian Mixture Models: The EM Algorithm in Practice

The EM Algorithm: An Intuitive Guide with the Coin Toss Example