Course Outline

Introduction

Setting up a Working Environment

Installing H2O

Anatomy of a Standard Machine Learning Workflow

  • Data-preprocessing, feature engineering, deployment, etc.

Statistical and Machine Learning Algorithms

  • Gradient boosted machines, generalized linear models, deep learning, etc.

How H2O Automates the Machine Learning Workflow

  • Binary Classification, Regression, etc.

Case Study: Predicting Product Availability

Downloading a Dataset

Building a Machine Learning Model

Specify a Training Frame

Training and Cross-Validating Different Models

Tuning the Hyperparameters

Training two Stacked Ensemble Models

Generating a Leaderboard of the Best Models

Inspecting the Ensemble Composition

Training many Deep Neural Network Models

Troubleshooting

Summary and Conclusion

Requirements

  • Experience working with machine learning models.
  • Python or R programming experience.

Audience

  • Data scientists
  • Data analysts
  • Subject matter experts (domain experts)
 14 Hours

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Price per participant

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