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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    • Introduction to Machine Learning with H2O-3 - Part 1

      Contains 15 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

      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

    • Introduction to Machine Learning with H2O-3 - Part 2

      Contains 12 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

      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

    • Introduction to Machine Learning with H2O-3 - Part 3

      Contains 11 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, and also with H2O Flow. 

      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, and also with H2O Flow.