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  • Third-Party Data: The Missing Ingredient to Predictive Modeling Success

    While the value of first-party data is undeniable, its use in machine learning to build predictive models undermines the ability to generate insights. In this white paper, discover the limitations of relying on first-party data to train algorithms and how to overcome them.

  • Increasing Predictive Modeling Accuracy with Data-Centric AI

    The majority of AI research concentrates on refining ML techniques, but it is often data quality, not ML techniques, determining predictive modeling accuracy. Read this e-book for a deep look into how data quality is crucial to building accurate predictive models, and why the budding data-centric AI approach is the new way forward.

  • Feature Engineering at a Glance

    When it comes to the data science lifecycle, feature engineering is a crucial part of model build and evaluation process. This expert guide will walk you through the importance of feature engineering and explore its opportunities. Access your copy today to learn more about the essentials of feature engineering within your ML and analytical models.

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