Machine Learning
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Making Sense of Unstructured Data: How to Evaluate Clustering?
Clustering is one of the most popular techniques for exploring and organizing unstructured data—like text, images, or customer behavior. But once you apply a clustering algorithm, how do you know if it actually worked well? In this post, we’ll break down the basics of clustering…
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Making Sense of Unstructured Data: Graph Theory
Unstructured data is often described as any data that does not have a predefined structure or is not easily organized into rows and columns like structured data. Examples include text, images, audio, and social media posts. This kind of data is abundant in today’s digital…
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Making Sense of Unstructured Data: K-means Clustering
Data is everywhere. From social media posts to customer reviews, much of the data we generate and collect is unstructured. Unlike structured data (like spreadsheets), unstructured data doesn’t follow a predefined format, making it harder to analyze. That’s where machine learning techniques like K-means clustering…
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Making Sense of Unstructured Data: Distance and Scaling Measures
In the age of big data, unstructured data like text, images, audio, and videos make up the bulk of the information we generate. Unlike structured data (like spreadsheets), unstructured data doesn’t come in neat rows and columns. To analyze it effectively, especially for machine learning…
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Making Sense of Unstructure Data: Dimensionality Reduction (PCA & tSNE)
What is Dimensionality? In data science, dimensionality refers to the number of features (also called variables or attributes) in a dataset. For example, an image that’s 28×28 pixels has 784 dimensions (28 multiplied by 28), because each pixel represents a feature. High-dimensional data presents challenges:…
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Making Sense of Unstructured Data: Unsupervised Learning
In today’s data-rich environment, most of the information we encounter is unstructured. From social media posts and support tickets to satellite imagery and audio recordings, unstructured data surrounds us. Making sense of this kind of data is critical for businesses, scientists, and engineers alike. One…
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Making Sense of Unstructured Data: Kullback-Leibler (KL) Divergence
In today’s data-driven world, most of the information we encounter is unstructured. From social media posts and emails to images and audio recordings, unstructured data surrounds us. Understanding and making sense of this data is crucial for businesses and researchers alike. One fundamental concept that…
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Making Sense of Unstructured Data – Covariance
In the world of data science, much of the information we encounter is messy, unpredictable, and without a clear structure. This is known as unstructured data, and it includes things like text, images, videos, and audio. Making sense of unstructured data is crucial for extracting…
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Making Sense Of Unstructure Data – Understanding Unsupervised Learning
In the world of data science and machine learning, algorithms are usually grouped into three main categories: In this post, we will focus on unsupervised learning. What is Unsupervised Learning? Unsupervised learning is a type of machine learning where computers learn from data that has…