Data Skeptic

Data Skeptic

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  • Typical duration

    37 min

    per episode

Based on Podcast App listening data

Data Skeptic episodes

  • Explainable K-Means

    In this episode, Kyle interviews Lucas Murtinho about the paper "Shallow decision treees for explainable k-means clustering" about the use of decision trees to help explain the clustering partitions.

    Check out our website for extended show notes! Thanks to our Sponsors:ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.
    26 min
  • Customer Clustering

    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!

    https://clear.ml/

    ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.

    23 min
  • k-means Image Segmentation

    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.

    Check out our website for extended show notes and images! 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.

    24 min
  • Tracking Elephant Clusters

    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

    27 min
  • k-means clustering
    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.

    25 min
  • Snowflake Essentials
    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."
    • Snowflake Essentials: Getting Started with Big Data in the Cloud by Frank Bell, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, Sameer Videkar
    • Snowflake Solutions
    • Snoptimizer - Snowflake Cost, Security, and Performance Optimization - Coming Soon!

    Thanks to our Sponsors:

    • Find Better Data Faster with Nomad Data. Visit nomad-data.com
    • Visit Springboard and use promo code DATASKEPTIC to receive a $750 discount
    47 min
  • Explainable Climate Science

    Zack Labe, a Post-Doctoral Researcher at Colorado State University, joins us today to discuss his work "Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles." Works Mentioned "Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles" by Zachary M. Labe, Elizabeth A. Barnes Sponsored by: Astrato and BBEdit by Bare Bones Software

    35 min
  • Energy Forecasting Pipelines

    Erin Boyle, the Head of Data Science at Myst AI, joins us today to talk about her work with Myst AI, a time series forecasting platform and service with the objective for positively impacting sustainability. https://docs.myst.ai/docs Visit Weights and Biases at wandb.me/dataskeptic Find Better Data Faster with Nomad Data. Visit nomad-data.com

    44 min
  • Matrix Profiles in Stumpy

    Sean Law, Principle Data Scientist, R&D at a Fortune 500 Company, comes on to talk about his creation of the STUMPY Python Library.

    Sponsored by Hello Fresh and mParticle:

    Go to Hellofresh.com/dataskeptic16 for up to 16 free meals AND 3 free gifts!

    Visit mparticle.com to learn how teams at Postmates, NBCUniversal, Spotify, and Airbnb use mParticle's customer data infrastructure to accelerate their customer data strategies.

    40 min

About Data Skeptic

From the publisher's feed

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying…

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