Data Skeptic

Have you ever wondered how you can use clustering to extract meaningful insight from a time-series single-feature data? In today’s episode, Ehsan speaks about his recent research on actionable feature extraction using clustering techniques. Want to find out more? Listen to discover the methodologies he used for his research and the commensurate results.

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Direct download: customer-clustering.mp3
Category:general -- posted at: 6:00am PDT

Linh Da joins us to explore how image segmentation can be done using k-means clustering.  Image segmentation involves dividing an image into a distinct set of segments.  One such approach is to do this purely on color, in which case, k-means clustering is a good option. 

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In the image below, you can see the k-means clustering segmentation results for the same image with the values of 2, 4, 6, and 8 for k.

Lilac Crowned Amazon

Direct download: k-means-image-segmentation.mp3
Category:general -- posted at: 4:00pm PDT

In today’s episode, Gregory Glatzer explained his machine learning project that involved the prediction of elephant movement and settlement, in a bid to limit the activities of poachers. He used two machine learning algorithms, DBSCAN and K-Means clustering at different stages of the project. Listen to learn about why these two techniques were useful and what conclusions could be drawn.

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Direct download: tracking-elephant-clusters.mp3
Category:general -- posted at: 2:43pm PDT

Welcome to our new season, Data Skeptic: k-means clustering.  Each week will feature an interview or discussion related to this classic algorithm, it's use cases, and analysis.

This episode is an overview of the topic presented in several segments.

Direct download: k-means-clustering.mp3
Category:general -- posted at: 8:44am PDT

Frank Bell, Snowflake Data Superhero, and SnowPro, joins us today to talk about his book “Snowflake Essentials: Getting Started with Big Data in the Cloud.” 

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Direct download: snowflake-essentials.mp3
Category:general -- posted at: 6:00am PDT