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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Interview With a Crypto Scam Investment Spammer

Krebs on Security

His resume also says he is a data science intern at Mondi Group , an Austrian manufacturer of sustainable packaging and paper. ” “He’s not even an information security specialist,” Quotpw said of Sergey. Mr. Proshutinskiy did not respond to requests for comment.

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GUEST ESSAY: The story behind how DataTribe is helping to seed ‘Cybersecurity Valley’ in Maryland

The Last Watchdog

GCIS was a Davos-level conference with no vendors and no selling, where scores of chief security information officers (CISOs), top CEO’s, industry and government thought leaders and leading innovators discussed the myriad challenges in and around cybersecurity and possible solutions in today’s environment.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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10 everyday machine learning use cases

IBM Big Data Hub

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

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How to build a successful AI strategy

IBM Big Data Hub

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. It will also determine the talent the organization needs to develop, attract or retain with relevant skills in data science, machine learning (ML) and AI development.

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Generative AI use cases for the enterprise

IBM Big Data Hub

Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing. Teamwork: Assemble a team with expertise in AI, data science and your industry. Data: High-quality, relevant data is the fuel that powers generative AI success.