H2O Driverless AI Starter Course
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Join Andreea Turcu, our Head of Global Training, as she guides you through the “Driverless AI Starter Course” by H2O ai. Dive into automated machine learning (AutoML) using H2O Driverless AI. This course is suitable for all levels and will provide you with the necessary skills to apply AutoML in your projects.
You’ll learn the basics of H2O Driverless AI, including setup, data import, and interface navigation. The course offers hands-on experience in data preprocessing, visualization, and analysis, preparing you for machine learning model development.
The course covers advanced topics like correlation graphs, prediction creation, and model performance optimization. You’ll learn to hyper-tune models, manage experiments, interpret results, and generate reports. Practical tasks are included for real-world practice.
Upon completion, you’ll not only be proficient in using H2O Driverless AI for AutoML but also gain skills in setting up environments, analyzing data, and utilizing advanced features like custom recipe creation and model fine-tuning. This course goes beyond theory, offering practical skills that can be applied in the real world. Additionally, you’ll be eligible for the H2O Driverless AI Certification. This certification is a testament to your proficiency and can significantly enhance your career prospects in the fields of AI and data science.
Enroll now to master automated machine learning with H2O Driverless AI.
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1DAI Starter Course - Class IntroVideo lesson
Start your learning journey with an engaging introduction to the "Driverless AI Starter Course" . Understand the goals and objectives of the course and get ready to explore the exciting world of automated machine learning.
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2What is Driverless AI?Video lesson
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3Problem TypesVideo lesson
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7Dataset Overview and Action ButtonsVideo lesson
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8Dataset DetailsVideo lesson
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9Modify by Recipe OverviewVideo lesson
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10Assignment 1 - Modify by Recipe OverviewVideo lesson
Welcome to the Modify Recipe using Live Code assignment in the Driverless AI Starter Course!
In this assignment, you will learn how to create a new dataset by utilizing the Modify By Recipe button and selecting the Live Code option. This task is designed to enhance your understanding of data manipulation and recipe customization within Driverless AI.
By completing this assignment, you will:Gain practical experience in modifying datasets using live code.
Understand how to customize data recipes to fit specific requirements.
Improve your skills in data preprocessing and manipulation within Driverless AI.
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11Visualize Action ButtonVideo lesson
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12Assignment 2 - Create a new visualisationVideo lesson
Welcome to the "Create a New Visualization" assignment in the Driverless AI Starter Course!
In this assignment, you will learn how to create a custom visualization using the Visualize action button in Driverless AI. This task is designed to help you master the visualization capabilities within the platform, enabling you to derive meaningful insights from your data.
By completing this assignment, you will:Gain hands-on experience in creating custom visualizations.
Learn how to use different plot types and features to explore your data.
Enhance your ability to interpret and present data insights effectively.
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13Correlation GraphVideo lesson
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14Assignment 3 - Explore the GraphsVideo lesson
Welcome to the "Explore the Graphs" assignment in the Driverless AI Starter Course!
In this assignment, you are encouraged to take a moment to delve into the visualization features available for the Credit_Score dataset. This exploration will help you become familiar with the various graph types and customization options that Driverless AI offers.
By completing this assignment, you will:
Enhance your understanding of the visualization capabilities in Driverless AI.
Learn how to effectively utilize different graph types to analyze and interpret data.
Improve your ability to customize visualizations to better present data insights.
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15Data Prep Action ButtonVideo lesson
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16Predict Action ButtonVideo lesson
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17Assignment 4 - Take the Interactive TourVideo lesson
Welcome to the "Take the Interactive Tour" assignment in the Driverless AI Starter Course!
In this assignment, you will explore the Predict action button of the Credit_Score.csv dataset. This interactive tour and exploration will familiarize you with the key features and functionalities of the prediction interface in Driverless AI.
By completing this assignment, you will:Gain a comprehensive understanding of the Predict action button and its functionalities.
Learn how to set up experiments with guided assistance, making the process more intuitive.
Enhance your knowledge of the prediction interface, enabling you to make informed decisions during your data analysis tasks.
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18Training SettingsVideo lesson
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19Expert Settings - High level OverviewVideo lesson
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20Custom RecipesVideo lesson
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21Assignment 5 - Create a New ExperimentVideo lesson
Welcome to the "Create a New Experiment" assignment in the Driverless AI Starter Course!
In this assignment, you will create a new experiment from the Datasets tab, modifying specific settings and comparing the results to a baseline experiment. This hands-on task will enhance your understanding of how different configurations affect experiment outcomes in Driverless AI.
By completing this assignment, you will:Learn how to create and configure new experiments in Driverless AI.
Understand the impact of different settings and scorers on experiment outcomes.
Develop skills in comparing and analyzing experiment results.
Gain the ability to fine-tune experiments to achieve better performance.
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22The Driverless AI Experiment PageVideo lesson
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23Variable importanceVideo lesson
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24Completed Experiment Listing PageVideo lesson
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25Focus on the ROC curveVideo lesson
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26Interpretability ReportVideo lesson
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27Shapley Values for Original FeaturesVideo lesson
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28Assignment 6 - Explore the Shapley Values OutputVideo lesson
Welcome to "Explore the Shapley Values Output" Assignment!
Shapley Values for Original Features (Naive Method):
Head over to the MLI (Model Interpretability) tab and locate the Shapley Values output for the original features.
Take a close look at the graph and explore the insights it provides.
Note that the output values may vary based on your specific experiment settings.
Additional Exploration (Optional):
On the right side of the screen, you’ll find options to search the dataset by Row ID or Row Number.
If successful, you can click the “Show Row” button to view further details about specific rows.
Refer to the screenshot on the left side for guidance.
By completing this assignment, you’ll gain valuable insights into feature importance and model interpretability. Feel free to explore and learn!
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29Partial Dependence PlotVideo lesson
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30Interpretations using Surrogate ModelsVideo lesson
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31Assignment 7 - Rerun the Baseline ExperimentVideo lesson
Welcome to the "Rerun the Baseline Experiment" assignment in the Driverless AI Starter Course!
In this assignment, you will revisit and enhance the initial Baseline experiment from the Experiments tab by dropping specific features: Name and Customer_ID. This exercise aims to demonstrate how altering feature sets can impact model performance and experiment outcomes in Driverless AI.
By completing this assignment, you will:Gain hands-on experience in modifying experiment settings and exploring feature impacts in Driverless AI.
Understand how feature selection and exclusion can influence model performance and outcomes.
Learn to interpret and compare experiment results to make informed decisions in data-driven scenarios.
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32Diagnostics and Visualize Scoring PipelineVideo lesson
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33Download AutoDocVideo lesson
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34Assignment 8 - Explore the AutodocVideo lesson
Welcome to the “Explore the Autodoc” assignment in the Driverless AI Starter Course!
In this assignment, you’ll delve into the Autodoc for the Baseline model. If you haven’t already, download the Autodoc and take a few minutes to explore it on your own. Then, try to answer the following questions:
Final Model Name and Type: What is the name of the final model, and what type of model is it?
Performance Metrics: In the subchapter “Performance” of the final model, identify the scorers that have the highest values in the Performance Table.
By completing this assignment, you’ll gain hands-on experience in modifying experiment settings and exploring feature impacts within Driverless AI. You’ll also learn how feature selection and exclusion can significantly influence model performance and outcomes.
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35Projects TabVideo lesson

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