H2O-3 Tutorials

Welcome to H2O, the AI platform for faster, more accurate Machine Learning. 

H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in an enterprise environment.

  • AUDIENCE : Jr. Data Scientist
  • LEARNING PATH LEVEL: Beginner
  • 3 Tasks


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    • 1A. Introduction to Machine Learning with H2O-3 - Classification

      Contains 18 Component(s)

      Learn how to use H2O-3's algorithms for a classification problem

      In this tutorial, you will learn how to use H2O's GLM, Random Forest, GBM models, and grid search to tune hyperparameters for a classification problem.

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. The H2O Flow tab is provided as complementary content for your personal knowledge. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.

      In this tutorial, you will learn how to use H2O's GLM, Random Forest, GBM models, and grid search to tune hyperparameters for a classification problem.

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. The H2O Flow tab is provided as complementary content for your personal knowledge. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.

    • 1B. Introduction to Machine Learning with H2O-3 - Regression

      Contains 15 Component(s)

      Learn how to use H2O-3's algorithms to solve a regression problem

      In this tutorial, you will learn how to use H2O's XGBoost and Deep Learning algorithms, as well as H2O's grid search to tune hyperparameters for a regression problem.

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. The H2O Flow tab is provided as complementary content for your personal knowledge. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.

      In this tutorial, you will learn how to use H2O's XGBoost and Deep Learning algorithms, as well as H2O's grid search to tune hyperparameters for a regression problem.

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. The H2O Flow tab is provided as complementary content for your personal knowledge. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.

    • 1C. Introduction to Machine Learning with H2O-3 - AutoML

      Contains 14 Component(s)

      Learn how to use H2O-3's AutoML to solve both a classification and a regression problem

      In this tutorial, you will learn how to use H2O's AutoML to solve a classification use case, and a regression use case, we will do this with Python or R, and also with H2O Flow. 

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.

      In this tutorial, you will learn how to use H2O's AutoML to solve a classification use case, and a regression use case, we will do this with Python or R, and also with H2O Flow. 

      You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

      You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

      If you have any feedback or question about the tutorial, please post it in the Discussion tab.