Mon, 28 February 2022
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. Visit our website for extended show notes! ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale. |
Mon, 21 February 2022
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. Thanks to our Sponsors: Visit Weights and Biases mention Data Skeptic when you request a demo! & Nomad Data 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. |
Fri, 18 February 2022
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. Click here to see additional show notes on our website! Thanks to our sponsor, Astrato |
Mon, 14 February 2022
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. |
Mon, 7 February 2022
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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