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"To get a job in machine learning, you must have the programming and mathematical knowledge to create artificial intelligence that is capable of learning new tasks without being explicitly coded. In an interview you may be asked about your experience with pertinent coding languages such as Java and C++ as well as with writing algorithms. The interview will be comprised mainly of technical questions that test your knowledge of the fundamental concepts of machine learning such as data mining and signal processing."

8,212 machine learning interview questions shared by candidates

Floy is a medical AI company on the mission to maximize human healthspan. Our first AI product helps radiologists to improve the diagnostic accuracy for lumbar spine examinations. Diagnostic errors are frighteningly common and statistically affect everyone of us in our lifetime. Radiologists have a 26.1% error rate of clinically relevant findings and 38% of these errors can be prevented through the collaboration of radiologists and AI. It is unacceptable that existing technology is not adopted in radiology. It is time to change that! Our goal is to find out how you approach problems, structure ML projects and obtain tangible results under time constraint. Code quality isless important in this exploratory challenge (production code is a different story). Build a minimal working pipeline with the framework (Tensorflow, PyTorch, etc) of your choice. 1) Structure your ML pipeline. Use different cells with comments to explain you approach. 2) Develop a working pipeline. It does not have to be perfect. Focus on getting things done. 3) Answer three qualitative questions in simple words.
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Machine Learning Engineer

Interviewed at Floy

1★
Mar 22, 2022

Floy is a medical AI company on the mission to maximize human healthspan. Our first AI product helps radiologists to improve the diagnostic accuracy for lumbar spine examinations. Diagnostic errors are frighteningly common and statistically affect everyone of us in our lifetime. Radiologists have a 26.1% error rate of clinically relevant findings and 38% of these errors can be prevented through the collaboration of radiologists and AI. It is unacceptable that existing technology is not adopted in radiology. It is time to change that! Our goal is to find out how you approach problems, structure ML projects and obtain tangible results under time constraint. Code quality isless important in this exploratory challenge (production code is a different story). Build a minimal working pipeline with the framework (Tensorflow, PyTorch, etc) of your choice. 1) Structure your ML pipeline. Use different cells with comments to explain you approach. 2) Develop a working pipeline. It does not have to be perfect. Focus on getting things done. 3) Answer three qualitative questions in simple words.

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