Automated machine learning is the process of automating some or all of the phases in a machine learning pipeline, such as data pre-processing, feature selection, algorithm selection, and hyper-parameter optimization. One advantage of these techniques is the empowerment of users, users that may or may not have data science expertise, allowing them to identify machine learning pipelines for their problems so that they achieve a high level of accuracy while at the same time minimizing the time spent on these problems.
During this presentation Vlad Iliescu will offer a high-level look of some of the available tools for automating machine learning, their advantages and disadvantages, before going into more depth on Microsoft’s Automated Machine Learning library. You will learn how to automatically train predictive models, which features are deemed important and which features are excluded, and also how you can take a peek under the hood of the auto-trained model. The model’s performance will be evaluated in an almost-real-world scenario, by competing in a live machine learning competition - Kaggle’s classic Titanic competition
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