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Training Better Models Using Automated Machine Learning

Training Better Models Using Automated Machine Learning

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

Vlad Iliescu

November 30, 2019
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  1. Training better models
    using Automated Machine
    Learning
    @vladiliescu
    Vladiliescu.net

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  2. ABOUT ME
    Head of AI, Strongbytes
    Microsoft mvp on ai

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  3. What is automated machine learning
    and why should I use it?

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  4. Microsoft’s automated ML

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  5. Get the code from
    github.com/vladiliescu/talks

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  6. The END
    @vladiliescu
    Vladiliescu.net

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