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Why Mathematics is Essential for Data Science and Machine Learning

insideBIGDATA

Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI. In this feature article, Daniel D.

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Securing Machine Learning Applications with Authentication and User Management

KDnuggets

A step-by-step guide to securing a FastAPI machine learning applications' endpoints with native authentication and user management.

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A Guide to Mastering Serverless Machine Learning

KDnuggets

Discover the what, why, and how of serverless machine learning with the interactive course GitHub repository.

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What is Discretization in Machine Learning?

Analytics Vidhya

Discretization is a fundamental preprocessing technique in data analysis and machine learning, bridging the gap between continuous data and methods designed for discrete inputs. appeared first on Analytics Vidhya.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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5 Breakthrough Machine Learning Research Papers Already in 2025

Machine Learning Mastery

Machine learning research continues to advance rapidly.

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Research: A periodic table for machine learning

Dataconomy

In machine learning, few ideas have managed to unify complexity the way the periodic table once did for chemistry. Now, researchers from MIT, Microsoft, and Google are attempting to do just that with I-Con, or Information Contrastive Learning. This ballroom analogy extends to all of machine learning.