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Hierarchical Clustering in Machine Learning: An In-Depth Guide

Pickl AI

Euclidean, Manhattan). Manhattan Distance : Sum of absolute differences across dimensions; useful when movement is restricted to grid-like paths. These clusters further merge into broader categories like vertebrates and invertebrates. In divisive clustering, split clusters based on dissimilarity criteria.

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Unlocking the Power of KNN Algorithm in Machine Learning

Pickl AI

Typical distance metrics include Euclidean distance, Manhattan distance, Minkowski distance, and cosine similarity. Text Categorisation: Utilising KNN, text data can be efficiently classified into predefined categories, aiding in tasks such as spam detection, sentiment analysis, and document classification.

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Classification vs. Clustering

Pickl AI

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. K-Nearest Neighbours (kNN) In order to calculate the distance between one data point and every other accomplished parameter through using the metrics of distance like Euclidean distance, Manhattan distance and others.

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Ask HN: Who is hiring? (July 2025)

Hacker News

Bonus: WMS platform experience (Manhattan, HighJump), TypeScript. Tech challenges: ReWMS, a custom WMS written in TypeScript, is optimized for condition grading pre-owned goods during receiving. ReWMS integrates with e-commerce platforms and 3PLs. Requirements: Strong experience with logistics/supply chain tech.

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The AI rules that US policymakers are considering, explained

Flipboard

One category of proposals deals with how AI systems interface with existing rules around copyright, privacy, and bias based on race, gender, sexual orientation, and disability. That — plus the sheer importance of an emerging technology like AI — makes it worth digging a little deeper into what action in DC might involve.

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