Feature Engineering en Español

Go to Course: https://www.coursera.org/learn/feature-engineering-es

Introduction

### Course Review: Feature Engineering en Español on Coursera If you are looking to upskill in the field of machine learning, "Feature Engineering en Español" on Coursera is a course that stands out for its comprehensive coverage of essential techniques in feature engineering using tools such as Vertex AI Feature Store, BigQuery ML, Keras, and TensorFlow. This course not only helps learners understand the theoretical concepts but also offers practical labs that facilitate hands-on experience in data preprocessing and feature transformation. #### Course Overview The course is structured to cater to individuals who wish to enhance the accuracy of their machine learning models by leveraging effective feature engineering techniques. Throughout the course, participants will explore the importance of both good and bad features, and gain insights into how to optimize their use in machine learning models. The curriculum is designed to move smoothly from introduction to advanced techniques, ensuring a well-rounded understanding of feature engineering. #### Syllabus Breakdown 1. **Introducción**: The course kicks off with an overview of the objectives, setting a clear path for what learners can expect to achieve. 2. **Introducción a Vertex AI Feature Store**: This module introduces Vertex AI Feature Store, a powerful tool that helps in managing features for machine learning models. Understanding this tool is crucial as it forms the backbone of the feature engineering process. 3. **De los datos sin procesar a los atributos**: Here, learners delve into the transformation of raw data into useful features. This stage often proves to be the most labor-intensive, and this module emphasizes how domain knowledge plays a vital role in effective feature creation. 4. **Ingeniería de atributos**: This module distinguishes between machine learning approaches and statistical methods, teaching students the nuances of performing feature engineering with BigQuery ML and Keras. Advanced practices in feature engineering are also explored. 5. **Procesamiento previo y creación de atributos**: Students will discover Dataflow, a complementary technology to Apache Beam, that aids in feature engineering and preprocessing. 6. **Combinaciones de atributos: TensorFlow Playground**: This unique module teaches how attribute combinations can significantly impact modern machine learning methods, a concept often underestimated in traditional approaches. 7. **Introducción a TensorFlow Transform**: Introducing TensorFlow Transform, this module details how to perform full data passes for preprocessing tasks, covering essential processes like normalization and input grouping. 8. **Resumen**: Finally, the course wraps up with a summary that reinforces key concepts and takeaways from the curriculum. #### Why You Should Enroll "Feature Engineering en Español" is highly recommended for data scientists, machine learning engineers, and anyone eager to weigh in on the significance of feature engineering in model performance. The blend of theory with practical labs ensures a thorough understanding while also providing the skills necessary to apply what you've learned directly to real-world scenarios. Additionally, given that the course is offered in Spanish, it allows native Spanish speakers or those who prefer learning in this language to access high-quality education without the language barrier. ### Conclusion In the competitive field of artificial intelligence and machine learning, mastering feature engineering can set you apart. This Coursera course provides the tools, techniques, and theoretical foundation necessary to excel. Whether you are a beginner looking to build your skills or an experienced professional wanting to polish your knowledge, this course is an invaluable resource for anyone looking to elevate their data science proficiency. Enroll now and harness the power of effective feature engineering!

Syllabus

Introducción

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

En este módulo, se presenta Vertex AI Feature Store.

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.

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.

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.

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.

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.