Automatic Machine Learning with H2O AutoML and Python

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En este proyecto guiado, tú:

Explain and describe automatic machine learning (AutoML)

Perform automatic machine learning with H2O AutoML and Python

Solve a business analytics problem with AutoML

Clock1.5 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipeline such as data pre-processing, feature engineering and model deployment. To successfully complete the project, we recommend that you have prior experience in Python programming, basic machine learning theory, and have trained ML models with a library such as scikit-learn. We will not be exploring how any particular model works nor dive into the math behind them. Instead, we assume you have this foundational knowledge and want to learn to use H2O's AutoML interface for automatic machine learning. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás


Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction and Project Overview

  2. Getting to Know Rhyme and the Marketing Data

  3. Load the Data

  4. Data Preprocessing & Start H2O

  5. Run H2O AutoML

  6. AutoML Leaderboard and Ensemble Exploration

  7. Base Learner Exploration

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso




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