Google Cloud

Feature Engineering en Español

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Gain insight into a topic and learn the fundamentals.
4.4

(37 reviews)

Intermediate level
Some related experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.4

(37 reviews)

Intermediate level
Some related experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Describir Vertex AI Feature Store y comparar los aspectos clave que debe tener un atributo útil

  • Realizar ingeniería de atributos con BigQuery ML, Keras y TensorFlow

  • Analizar cómo procesar previamente y explorar atributos con Dataflow y Dataprep

  • Usar tf.Transform

Details to know

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Assessments

6 assignments

Taught in Spanish

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This course is part of the Machine Learning with TensorFlow on Google Cloud en Español Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Develop job-relevant skills with hands-on projects
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There are 8 modules in this course

En este módulo, se brinda una descripción general del curso y sus objetivos.

What's included

1 video

En este módulo, se presenta Vertex AI Feature Store.

What's included

6 videos1 reading1 assignment

La ingeniería de atributos suele ser la fase más larga y difícil de la creación de proyectos de AA. En el proceso de ingeniería de atributos, se comienza con los datos sin procesar y se utiliza el propio conocimiento del dominio para crear atributos que hagan funcionar los algoritmos de aprendizaje automático. En este módulo, exploramos qué elementos son buenos atributos y cómo representarlos en un modelo de AA.

What's included

9 videos1 reading1 assignment

En este módulo, se analizan las diferencias entre el aprendizaje automático y las estadísticas, y cómo realizar ingeniería de atributos en BigQuery ML y Keras. También abordaremos algunas prácticas avanzadas de ingeniería de atributos.

What's included

12 videos1 reading1 assignment3 app items

En este módulo, aprenderás más sobre Dataflow, una tecnología complementaria a Apache Beam. Ambas soluciones pueden ayudar a crear y ejecutar el procesamiento previo y la ingeniería de atributos.

What's included

3 videos1 reading1 assignment

En el aprendizaje automático tradicional, las combinaciones de atributos no desempeñan un rol significativo. Sin embargo, en los métodos modernos de AA, estas son una parte invaluable de tu kit de herramientas. En este módulo, aprenderás a reconocer los tipos de problemas en los que las combinaciones de atributos son un medio potente para facilitar el aprendizaje automático.

What's included

5 videos1 reading1 assignment

TensorFlow Transform (tf.Transform) es una biblioteca para el procesamiento previo de datos con TensorFlow que resulta útil cuando este proceso requiere un pase completo de datos. Por ejemplo, normalizar un valor de entrada según la media y la desviación estándar, generar números enteros a partir del vocabulario analizando valores en todos los ejemplos de entrada y agrupar las entradas según la distribución de datos observada. En este módulo, explicaremos los casos de uso de tf.Transform.

What's included

5 videos1 reading1 assignment

Este módulo es un resumen del curso Feature Engineering.

What's included

4 readings

Instructor

Instructor ratings
4.0 (6 ratings)
Google Cloud Training
Google Cloud
1,693 Courses2,814,848 learners

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Google Cloud

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4.4

37 reviews

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