Welcome to Driverless AI Tutorials

H2O.ai created AI Tutorials out of inspiration for democratizing open source, distributed machine learning. Our tutorials are open to anyone in the community who would like to learn Distributed Machine Learning through step-by-step tutorials. Tutorials housed here are targeted at people of all skill levels.

  • AUDIENCE : Jr. Data Scientist
  • LEARNING PATH LEVEL: Beginner, Intermediate
  • 10 Tasks


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Tutoriales de Driverless AI (EspaƱol)

Expande tu conocimiento al completar los siguientes tutoriales de Driverless AI

  • Tutorial 0: Getting Started with Driverless AI Test Drive

    Contains 8 Component(s)

    Step by step guide to help you set-up your environment

    This tutorial will guide you to set-up your Driverless AI Test Drive environment in order to continue with the other tutorials. Test Drive is a two-hour lab session that exists in H2O's Aquarium, a cloud environment that provides access to various tools for workshops, conferences, and training. 

    This tutorial will guide you to set-up your Driverless AI Test Drive environment in order to continue with the other tutorials. Test Drive is a two-hour lab session that exists in H2O's Aquarium, a cloud environment that provides access to various tools for workshops, conferences, and training. 

  • Tutorial 1A: Automatic Machine Learning Introduction with Driverless AI

    Contains 15 Component(s)

    Launch your first experiment in Driverless AI Test Drive

    In this tutorial, we will be working with the Titanic dataset to try to predict if someone could survive or not from the perspective of a passenger life insurance company. You will be exploring how to auto visualize a dataset, launch an experiment, perform feature engineering and create a ML Interpretability report

    In this tutorial, we will be working with the Titanic dataset to try to predict if someone could survive or not from the perspective of a passenger life insurance company. You will be exploring how to auto visualize a dataset, launch an experiment, perform feature engineering and create a ML Interpretability report

  • Tutorial 1B: Machine Learning Experiment Scoring and Analysis Tutorial - Financial Focus

    Contains 16 Component(s)

    Deeper Analysis of a Classification Model

    In this tutorial, we will be working with a subset of the Freddie Mac Single-Family Loan-Level Dataset to build a Classification model. You will be exploring how to evaluate a DAI model through tools like ROC, Prec-Recall, Gain and Lift Charts, K-S Chart as well as metrics such as AUC, F-Scores, GINI, MCC, and Log Loss.

    In this tutorial, we will be working with a subset of the Freddie Mac Single-Family Loan-Level Dataset to build a Classification model. You will be exploring how to evaluate a DAI model through tools like ROC, Prec-Recall, Gain and Lift Charts, K-S Chart as well as metrics such as AUC, F-Scores, GINI, MCC, and Log Loss.

  • Tutorial 1C: Machine Learning Interpretability Tutorial

    Contains 13 Component(s)

    Learn how to interpret the results from Driverless AI

    In this tutorial, we will be working with a default of credit card clients dataset in order to understand and be able to interpret the results from Driverless AI. You will also explore how to launch an experiment, create ML Interpretability report, explore explainability concepts such as Global Shapley, partial dependence plot, decision tree surrogate, K-LIME, Local Shapley, LOCO and individual conditional expectations. 

    In this tutorial, we will be working with a default of credit card clients dataset in order to understand and be able to interpret the results from Driverless AI. You will also explore how to launch an experiment, create ML Interpretability report, explore explainability concepts such as Global Shapley, partial dependence plot, decision tree surrogate, K-LIME, Local Shapley, LOCO and individual conditional expectations. 

  • Tutorial 2A: Time Series Recipe Tutorial - Retail Sales Forecasting

    Contains 12 Component(s)

    Learn how to set up a Time Series Experiment and how to explore the results.

    In this tutorial, you will learn what a time series experiment is, concept behind time series, how to set a time series experiment on Driverless AI, time series and more. 

    In this tutorial, you will learn what a time series experiment is, concept behind time series, how to set a time series experiment on Driverless AI, time series and more. 

  • Tutorial 2B: Natural Language Processing Tutorial - Sentiment Analysis

    Contains 13 Component(s)

    Learn how to apply automatic machine learning to build a model to classify customer reviews

    In this tutorial, you will learn how to launch a sentiment analysis experiment, walk through sentiment analysis experiment settings, NLP concepts, Driverless AI NLP Recipe and more.

    In this tutorial, you will learn how to launch a sentiment analysis experiment, walk through sentiment analysis experiment settings, NLP concepts, Driverless AI NLP Recipe and more.

  • Tutorial 3A: Get Started with Open Source Custom Recipes Tutorial

    Contains 12 Component(s)

    Take more control of your experiment, and learn how to use custom recipes in Driverless AI

    In this tutorial, you will learn what a recipe is, the types of open source recipes that are available on Driverless AI 1.9.0 and how to load and run them in experiments.

    In this tutorial, you will learn what a recipe is, the types of open source recipes that are available on Driverless AI 1.9.0 and how to load and run them in experiments.

  • Tutorial 3B: Build Your Own Custom Recipe Tutorial

    Contains 13 Component(s)

    Learn how you can build your own custom recipe to enhance Driverless AI

    In this tutorial, you will learn how to build and troubleshoot your own custom transformer, scorer, and model recipes. 

    In this tutorial, you will learn how to build and troubleshoot your own custom transformer, scorer, and model recipes. 

  • Tutorial 4A: Scoring Pipeline Deployment Introduction

    Contains 10 Component(s)

    Learn how to deploy models from Driverless AI

    By the end of this tutorial, you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI.

    By the end of this tutorial, you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI.

  • Tutorial 4B: Scoring Pipeline Deployment Templates

    Contains 9 Component(s)

    By the end of this tutorial, you will predict the cooling condition for a Hydraulic System by deploying a MOJO Scoring Pipeline into production using a Driverless AI Deployment Template.

    This tutorial is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI's Production Ready Deployment Templates.

    This tutorial is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI's Production Ready Deployment Templates.