Daniel has a background in Machine Learning, Statistics and Data Science and how these are applied to imaging, recommender systems, digital advertising/marketing, and biological and physical sciences. He has over 30 peer reviewed publications in academic journals and is based in Washington D.C.
How you can efficiently select the appropriate model for your data using various variable/model selection methods.
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