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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 machine learning? This post will dive deeper into the nuances of each field.

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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Exploratory data science and visualization: Access Iceberg tables through auto-discovered CDW connection in CML projects.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Machine Learning algorithms often need to handle highly-imbalanced datasets. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. Their tests are performed using C4.5-generated Chawla et al., 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling.

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Data Science at The New York Times

Domino Data Lab

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Latest in machine learning research centers rapidly expanding in Africa? He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything. Latest in quantum physics? Roll the clock out to Sci Foo.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. That presented an opportunity to learn, putting me in the same position as much of the audience. Newer work in machine learning (e.g., We keep feeding the monster data.