Feature Engineering en Español from Coursera

What's inside

Syllabus

Introducción

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.

Resumen

Este módulo es un resumen del curso Feature Engineering.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

El programa aprueba el uso de BigQuery ML, Keras y TensorFlow

Ofrece oportunidades para la práctica a través de laboratorios sobre ingeniería de características

Se concentra en la ingeniería de atributos, un componente crucial para modelos de AA efectivos

Requiere experiencia previa en aprendizaje automático y estadísticas

Impartido por Google Cloud Training, reconocido por su experiencia en la nube

Reviews summary

Spanish feature engineering

This course will provide you with a strong foundation in Feature Engineering with plenty of practical examples. Reviewers particularly enjoyed the integration with BigQuery ML, Keras, and TensorFlow. Other highlights include learning about Vertex AI Feature Store, preprocessing, and attribute creation. Be prepared to encounter some theoretical content. Overall, learners found this course to be insightful and practical.

Integrates with BigQuery ML, Keras, and TensorFlow.

"El curso incluye contenido y labs sobre la ingeniería de atributos en los que se usan BigQuery ML, Keras y TensorFlow."

Many practical examples to enhance learning.

"Muy interesante"

Introduces Vertex AI Feature Store.

"Introducción a Vertex AI Feature Store"

Some theoretical content to be aware of.

"Demasiado teórico"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Feature Engineering en Español with these activities:

Crear un resumen de los principales tipos de atributos

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Mejorar la comprensión de los diferentes tipos de atributos y su importancia en la ingeniería de atributos.

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Definir y clasificar los tipos de atributos
Proporcionar ejemplos de cada tipo de atributo
Describir cómo se utilizan los diferentes tipos de atributos en el aprendizaje automático

Participar en discusiones sobre ingeniería de atributos

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Comprender diversas perspectivas y abordar conceptos desafiantes mediante la interacción con compañeros.

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Participar en foros o grupos de estudio en línea
Asistir a sesiones de estudio grupales
Comentar y proporcionar comentarios sobre el trabajo de los demás

Realizar ejercicios de transformación de datos

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Fortalecer las habilidades de ingeniería de atributos mediante la práctica de transformación de datos.

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Utilizar BigQuery ML y TensorFlow para transformar datos
Aplicar técnicas de normalización y estandarización
Experimentar con diferentes técnicas de codificación

Three other activities

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Explorar los casos de uso de TensorFlow Transform

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Ampliar el conocimiento de las técnicas avanzadas de ingeniería de atributos mediante el uso de TensorFlow Transform.

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Seguir tutoriales sobre TensorFlow Transform
Investigar los diferentes casos de uso de TensorFlow Transform
Aplicar TensorFlow Transform a un proyecto de ejemplo

Desarrollar una canalización de procesamiento previo y creación de atributos

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Aplicar habilidades de ingeniería de atributos para crear una canalización funcional y efectiva.

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Diseñar la canalización de procesamiento previo y creación de atributos
Implementar la canalización utilizando Apache Beam o Dataflow
Evaluar el rendimiento de la canalización

Contribuir a proyectos de código abierto relacionados con la ingeniería de atributos

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Aplicar conocimientos y habilidades a través de la colaboración en proyectos de código abierto.

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Identificar proyectos de código abierto relevantes para la ingeniería de atributos
Aportar correcciones de errores, nuevas funciones o mejoras de documentación
Colaborar con otros contribuyentes y mantener el proyecto

Career center

Learners who complete Feature Engineering en Español will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge in programming and scientific methods to study data. This includes gathering, cleaning, managing, and analyzing data using sophisticated tools and scripts. They build predictive and prescriptive models based on data to help businesses make better decisions. This course may be helpful for Data Scientists as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. They work with Data Scientists to identify and solve business problems using machine learning. This course may be helpful for Machine Learning Engineers as it will teach them how to use Feature Engineering to improve the accuracy of their models.

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Statistician

Statisticians use their knowledge of statistics to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and education. This course may be helpful for Statisticians as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Data Analyst

Data Analysts use their knowledge of data analysis tools and techniques to extract insights from data. They work with businesses to help them make better decisions. This course may be helpful for Data Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics and data analysis to help businesses improve their operations. They work with businesses to identify and solve problems, and they develop and implement solutions. This course may be helpful for Operations Research Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Business Analyst

Business Analysts use their knowledge of business and data analysis to help businesses improve their operations. They work with businesses to identify and solve problems, and they develop and implement solutions. This course may be helpful for Business Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Data Engineer

Data Engineers are responsible for building and maintaining data pipelines that collect, process, and store data for analysis by Data Scientists. They ensure the data is clean, reliable, and accessible for data analysis and reporting. This course may be helpful for Data Engineers as it will teach them how to use Feature Engineering to process and transform data for use in models.

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Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics and data analysis to help businesses make investment decisions. They work with businesses to analyze financial data and make recommendations on how to invest their money. This course may be helpful for Quantitative Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Financial Analyst

Financial Analysts use their knowledge of finance and data analysis to help businesses make investment decisions. They work with businesses to analyze financial data and make recommendations on how to invest their money. This course may be helpful for Financial Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Marketing Analyst

Marketing Analysts use their knowledge of marketing and data analysis to help businesses develop and implement marketing campaigns. They work with businesses to identify and target their target audience, and they develop and implement marketing campaigns to reach that audience. This course may be helpful for Marketing Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Risk Analyst

Risk Analysts use their knowledge of risk management and data analysis to help businesses identify and mitigate risks. They work with businesses to develop and implement risk management plans. This course may be helpful for Risk Analysts as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development tools to build and maintain applications that meet the needs of users. This course may be helpful for Software Engineers as it will teach them how to use Feature Engineering to improve the accuracy of their models.

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Product Manager

Product Managers are responsible for the development and launch of new products and features. They work with engineers, designers, and marketers to bring new products to market. This course may be helpful for Product Managers as it will teach them how to use Feature Engineering to analyze attributes and improve the accuracy of their models.

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