Programa del Curso

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

Requerimientos

  • Experiencia trabajando con modelos de aprendizaje automático.
  • Python o experiencia en programación en R.

Audiencia

  • Científicos de datos
  • Analistas de datos
  • Expertos en la materia (expertos en el dominio)
 14 horas

Número de participantes



Precio por participante

Cursos Relacionados

Categorías Relacionadas