Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Machine learning derives mathematical models from raw data. In the model building process, raw data is first processed into "features," then the features are given to algorithms to train a model. The process of turning raw data into features is sometimes called feature engineering, and it is a crucial step in model building. Good features lead to successful models with a lot of predictive power; bad features lead to a lot of headache and nowhere. This talk aims to help the audience understand what is a feature space and why it is so important. We will go through some common feature space representations of English text and discuss what tasks they are suited for and why. Expect lots of pictures, whiteboard drawings and handwaving. We will exercise our power of imagination to visualize high dimensional feature spaces in our mind's eye. Presented by Alice Zheng Director of Data Science at Dato.