From the course: Data Science Foundations: Fundamentals
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AutoML
From the course: Data Science Foundations: Fundamentals
AutoML
- [Instructor] Working with data can be challenging under the best of circumstances and there's a lot of thankless work that goes into it. For example, there's the common saying that 80% of the time of any data project is spent getting the data prepared and that certainly matches with my experience. And the data preparation tasks involve things like converting categorical features or variables to a numerical format, or dealing with missing data or rescaling the data, or the complicated procedures of feature engineering, feature extraction, and feature selection which are central to building a machine learning model. Also, when you're doing machine learning, there is the difficult matter of hyperparameters. These are the settings that are used for the various algorithms. So they're like the knobs and the switches you have to do before you can actually have the data analyzed. Now, sometimes these are pretty simple. For linear…
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