Google Cloud

Feature Engineering en EspaƱol

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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 Ćŗtil

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

  • Analizar cĆ³mo 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

Build your subject-matter expertise

This course is part of the Machine Learning with TensorFlow on Google Cloud en EspaƱol Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
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There are 8 modules in this course

En este mĆ³dulo, se brinda una descripciĆ³n general del curso y sus objetivos.

What's included

1 video

En este mĆ³dulo, se presenta Vertex AI Feature Store.

What's included

6 videos1 reading1 assignment

La ingenierĆ­a de atributos suele ser la fase mĆ”s larga y difĆ­cil de la creaciĆ³n de proyectos de AA. En el proceso de ingenierĆ­a 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 automĆ”tico. En este mĆ³dulo, exploramos quĆ© elementos son buenos atributos y cĆ³mo representarlos en un modelo de AA.

What's included

9 videos1 reading1 assignment

En este mĆ³dulo, se analizan las diferencias entre el aprendizaje automĆ”tico y las estadĆ­sticas, y cĆ³mo realizar ingenierĆ­a de atributos en BigQuery ML y Keras. TambiĆ©n abordaremos algunas prĆ”cticas avanzadas de ingenierĆ­a de atributos.

What's included

12 videos1 reading1 assignment3 app items

En este mĆ³dulo, aprenderĆ”s mĆ”s sobre Dataflow, una tecnologĆ­a complementaria a Apache Beam. Ambas soluciones pueden ayudar a crear y ejecutar el procesamiento previo y la ingenierĆ­a de atributos.

What's included

3 videos1 reading1 assignment

En el aprendizaje automĆ”tico tradicional, las combinaciones de atributos no desempeƱan un rol significativo. Sin embargo, en los mĆ©todos modernos de AA, estas son una parte invaluable de tu kit de herramientas. En este mĆ³dulo, aprenderĆ”s a reconocer los tipos de problemas en los que las combinaciones de atributos son un medio potente para facilitar el aprendizaje automĆ”tico.

What's included

5 videos1 reading1 assignment

TensorFlowĀ Transform (tf.Transform) es una biblioteca para el procesamiento previo de datos con TensorFlow que resulta Ćŗtil cuando este proceso requiere un pase completo de datos. Por ejemplo, normalizar un valor de entrada segĆŗn la media y la desviaciĆ³n estĆ”ndar, generar nĆŗmeros enteros a partir del vocabulario analizando valores en todos los ejemplos de entrada y agrupar las entradas segĆŗn la distribuciĆ³n de datos observada. En este mĆ³dulo, explicaremos los casos de uso de tf.Transform.

What's included

5 videos1 reading1 assignment

Este mĆ³dulo es un resumen del curso Feature Engineering.

What's included

4 readings

Instructor

Instructor ratings
4.0 (6 ratings)
Google Cloud Training
Google Cloud
1,788 Courses3,260,003 learners

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4.4

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