Routine
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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:…