¿Desea saber sobre Vertex AI Feature Store? ¿Desea saber cómo mejorar la exactitud de los modelos de AA o averiguar qué columnas de datos crean los atributos más útiles? Le damos la bienvenida a Feature Engineering, donde analizaremos los atributos buenos y malos, y cómo se los puede procesar previamente y transformar para aprovecharlos al máximo en sus modelos. El curso incluye contenido y labs sobre la ingeniería de atributos en los que se usan BigQuery ML, Keras y TensorFlow.
This course is part of the Machine Learning with TensorFlow on Google Cloud en Español Specialization
Feature Engineering en Español
About this Course
What you will 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
Skills you will gain
- Python Programming
- Machine Learning
- Feature Engineering
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Syllabus - What you will learn from this course
En este módulo, se brinda una descripción general del curso y sus objetivos.
Introducción a Vertex AI Feature Store
En este módulo, se presenta Vertex AI Feature Store.
De los datos sin procesar a los atributos
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.
Ingeniería de atributos
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.
Procesamiento previo y creación de atributos
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.
Combinaciones de atributos: TensorFlow Playground
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.
Introducción a TensorFlow Transform
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.
Este módulo es un resumen del curso Feature Engineering.
- 5 stars70.83%
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TOP REVIEWS FROM FEATURE ENGINEERING EN ESPAÑOL
Mu y interesante y practico. Aclara los conceptos para preparación de datos
it is hard with so many new tools, I will have to watch it again if anytime i need to use it
About the Machine Learning with TensorFlow on Google Cloud en Español Specialization
¿Qué es el aprendizaje automático? ¿Qué tipos de problemas puede solucionar? ¿Cuáles son las cinco fases para convertir un posible caso práctico en un recurso que pueda aprovechar la tecnología de aprendizaje automático? ¿Por qué es importante no saltarse fases? ¿Por qué las redes neuronales son tan populares? ¿Cómo plantear un problema de aprendizaje supervisado y encontrar una buena solución generalizable mediante un descenso de gradientes y una forma meditada de crear conjuntos de datos? Aprenda a escribir modelos de aprendizaje automático distribuido que escalen en Tensorflow y que brinden predicciones de alto rendimiento. Convierta los datos sin procesar en funciones de una forma que permita al AA aprender características importantes de los datos y aportar una percepción humana para abordar los problemas. Por último, aprenda a incorporar la combinación adecuada de parámetros que desarrolle modelos generalizados y exactos, y conozca la teoría para solucionar determinados tipos de problemas de AA. Experimentará con el AA de extremo a extremo, a partir de la construcción de una estrategia centrada en el AA y el avance hacia el entrenamiento, optimización y producción de modelos con labs prácticos mediante Google Cloud Platform.
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