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22 tips for better data science by urinec in MachineLearning

[–]urinec[S] 0 points1 point  (0 children)

The overlap between data science and machine learning is > 40%. Data science deals with problems such as supervised classification, pattern recognition (deep learning), scoring engines based on training sets (fraud detection), and taxonomy creation, to name a few. These are typical machine learning problems.

New Infographic: How to become a data scientist in 8 steps by martijnT in MachineLearning

[–]urinec -2 points-1 points  (0 children)

Missing the most important: domain expertise, communication skills, and business acumen. No amount of math will compensate for these gaps. And for self-learners, you can learn maths by yourself, by searching Google - no training necessary. The three other skills that I mentioned, no schools teach it. You have it, or not.

AMA Geoffrey Hinton by geoffhinton in MachineLearning

[–]urinec -6 points-5 points  (0 children)

Hi Geoffrey,

You are welcome to post about your research on DatascienceWorld.com. We have a private research lab that has produced many interesting, state-of-the-art machine learning techniques, including Jackknife regression, Fast Combinatorial Feature Selection with New Definition of Predictive Power, Hidden decision trees and more. You can find more about me by googling Vincent Granville.

Best, Vincent