Google Cloud

Feature Engineering en Español

This course is part of Machine Learning with TensorFlow on Google Cloud en Español Specialization

Taught in Spanish

3,112 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(31 reviews)

Intermediate level
Some related experience required
14 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Usar Vertex AI Feature Store

  • Describir cómo ir de los datos sin procesar a los atributos y realizar ingeniería de atributos

  • Realizar el procesamiento previo de atributos con Apache Beam y Cloud DataFlow

  • Usar tf.Transform

Details to know

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Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(31 reviews)

Intermediate level
Some related experience required
14 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

8 videos1 reading1 quiz1 app item

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 quiz

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 quiz4 app items

En este módulo, aprenderá 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 quiz

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 su kit de herramientas. En este módulo, aprenderá 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 quiz

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

6 videos1 reading1 quiz1 app item

Este módulo es un resumen del curso Feature Engineering.

What's included

4 readings

Instructor

Instructor ratings
3.8 (5 ratings)
Google Cloud Training
Google Cloud
1,370 Courses2,547,951 learners

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Google Cloud

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4.4

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