Sentence examples for building a machine learning from inspiring English sources
Onehot Labs is a healthcare technology startup building a machine learning service from proprietary data that will eliminate human inefficiencies of medical billing cycle.
Lopp says the Kosei team will spend their first 90 days figuring out where to apply themselves across black ops spam deterrence, product discovery and recommendations, visual object recognition, ad click prediction for monetization, growth analytics, and building a machine learning system on spark for Pinterest's data team.
This paper studies the problem of building a machine learning method for biological data.
Let's say you're a bank building a machine learning model to predict who will be most likely to repay their loan quickly.
It'll surely still be a while before we see a "get started with programming in Python by building a machine learning model" course.
This is not as we usually do when building a machine learning model.
With the constant readouts providing more detailed data, Snyder's group saw not only that glucose dysregulation is more common than previously thought, but they also used the data to start building a machine-learning model to predict the specific foods to which people spike.
With the frequent readouts providing more detailed data, Snyder's group saw not only that glucose dysregulation is more common than previously thought, but they also used the data to start building a machine-learning model to predict the specific foods to which people spike.
Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy.
So, Krakow and her team assembled the medical data of 350 relapse patients, 1,000 pages per, and put it toward building a machine-learning algorithm that could predict the best treatment sequence for any patient, at any point in time.
We exploited these associations by building a machine-learning model that prioritizes candidate genes according to their inferred HI probabilities.
