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¿Cuáles son las prácticas recomendadas para implementar el aprendizaje automático en Google Cloud? ¿Qué es Vertex AI y cómo se puede utilizar la plataforma para crear, entrenar e implementar rápidamente modelos de aprendizaje automático de AutoML sin escribir una sola línea de código? ¿Qué es el aprendizaje automático? ¿Qué tipos de problemas puede solucionar?
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¿Cuáles son las prácticas recomendadas para implementar el aprendizaje automático en Google Cloud? ¿Qué es Vertex AI y cómo se puede utilizar la plataforma para crear, entrenar e implementar rápidamente modelos de aprendizaje automático de AutoML sin escribir una sola línea de código? ¿Qué es el aprendizaje automático? ¿Qué tipos de problemas puede solucionar?
Read more
¿Cuáles son las prácticas recomendadas para implementar el aprendizaje automático en Google Cloud? ¿Qué es Vertex AI y cómo se puede utilizar la plataforma para crear, entrenar e implementar rápidamente modelos de aprendizaje automático de AutoML sin escribir una sola línea de código? ¿Qué es el aprendizaje automático? ¿Qué tipos de problemas puede solucionar?
Google considera que el aprendizaje automático es diferente: se trata de proporcionar una plataforma unificada para conjuntos de datos administrados, un almacén de atributos, una forma de crear, entrenar e implementar modelos de aprendizaje automático sin escribir una sola línea de código, así como proporcionar la capacidad de etiquetar datos y crear notebooks de Workbench utilizando frameworks como TensorFlow, SciKit-learn, Pytorch, R y otros. Vertex AI Platform también ofrece la posibilidad de entrenar modelos personalizados, crear canalizaciones de componentes y realizar predicciones en línea y por lotes. Además, analizamos las cinco fases para convertir un posible caso de uso en un recurso que pueda aprovechar la tecnología del aprendizaje automático y estudiamos por qué es importante no saltarse estas fases. Finalizamos con un reconocimiento de los sesgos que el aprendizaje automático puede amplificar y cómo reconocerlos.
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What's inside
Syllabus
Module 0: Introducción al curso y a la serie
Este módulo es una introducción a la serie del curso y a los expertos de Google que lo impartirán.
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Syllabus
Module 0: Introducción al curso y a la serie
Este módulo es una introducción a la serie del curso y a los expertos de Google que lo impartirán.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Aprenden procesos de aprendizaje automático en Vertex AI, una plataforma de Google Cloud
Aprenden a crear, entrenar e implementar rápidamente modelos de Machine Learning AutoML
Entienden las mejores prácticas para implementar Machine Learning en Google Cloud
Conocen los cinco pasos para transformar un posible caso de uso en un recurso que aprovecha la tecnología de Machine Learning
Comprenden la importancia de no saltarse las cinco fases para transformar un caso de uso en un recurso que aprovecha el Machine Learning
Reconocen los sesgos que el Machine Learning puede amplificar y cómo identificarlos
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Reviews summary
El enfoque de google en ml con vertex ai
Según los estudiantes, este curso ofrece una visión clara de cómo Google aplica el Aprendizaje Automático y presenta la plataforma Vertex AI. Muchos aprecian que el contenido esté disponible íntegramente en español, lo que lo hace accesible para hispanohablantes. Destacan la perspectiva práctica de Google y los consejos basados en la experiencia real de sus expertos. Algunos lo consideran una excelente introducción a la filosofía de ML de Google y al uso básico de Vertex AI, especialmente si no tienes experiencia previa con la plataforma. Si bien es un buen punto de partida para entender la visión de Google y las capacidades de Vertex AI, algunos advierten que el nivel de detalle puede ser algo general para quienes buscan profundizar en aspectos técnicos o de implementación de modelos.
Disponible completamente en español, facilitando el aprendizaje.
"¡Excelente que esté completamente en español! Hace que los temas técnicos sean más fáciles de digerir para mí."
"Poder aprender estos conceptos complejos en mi idioma nativo es una gran ventaja de este curso..."
"La traducción y localización parecen de alta calidad, lo que es importante para la comprensión."
Explica el enfoque único de Google hacia el Aprendizaje Automático.
"Me encantó entender cómo Google piensa sobre el ML, sus mejores prácticas y fases del proyecto desde una perspectiva real."
"Los conocimientos compartidos por los expertos de Google son muy valiosos, ofrecen una visión interna que no encuentras en otros lugares."
"La parte sobre IA responsable fue muy pertinente y muestra la seriedad del enfoque de Google hacia los sesgos."
Buen primer vistazo a la plataforma ML de Google Cloud.
"El curso me dio una visión general muy útil de qué es Vertex AI y cómo se usa en Google..."
"Aprendí a utilizar la plataforma para crear y entrenar modelos sin código, lo cual es genial para empezar."
"La forma en que presentan Vertex AI es muy clara, aunque me gustaría ver ejemplos más avanzados en el futuro."
"Me ayudó a comprender el ecosistema de Vertex AI y sus componentes principales."
Fuerte en conceptos, menos en la implementación práctica profunda.
"Me pareció que el curso se centra mucho en los conceptos y la 'filosofía', pero carece de laboratorios prácticos o demos a fondo."
"Aunque explican cómo Google *lo hace*, no sentí que me dieran las herramientas para replicarlo paso a paso en mi entorno de trabajo."
"Es más teórico y de visión general que un curso práctico de 'cómo se hace'."
Puede ser algo general para quienes buscan detalle técnico.
"Es un buen punto de partida, pero si ya tienes experiencia en ML, puede sentirse un poco superficial en algunos módulos."
"Esperaba más detalles técnicos sobre la implementación de modelos o código; es más bien una vista de alto nivel y conceptual."
"Ideal para principiantes o gerentes que quieren entender el panorama, menos para ingenieros que necesitan ejemplos de código y configuración detallada."
Activities
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during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in How Google does Machine Learning en Español with these
activities:
Organize course materials
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Improve your understanding and retention of course materials by organizing them effectively.
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-
Review notes and assignments
-
Create summaries
-
Organize materials digitally
Read "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
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Gain a solid understanding of machine learning concepts and techniques.
View
Aprende Machine Learning con Scikit-Learn,...
on Amazon
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Read the book
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Work through the exercises
Review linear algebra
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Strengthen your foundational understanding of linear algebra, a crucial skill for machine learning.
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Solve practice problems
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Enhance your understanding of Vertex AI's features and functionalities through hands-on tutorials.
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Acquire practical knowledge and guidance from experts by attending a Vertex AI workshop.
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Vertex AI
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Find a relevant workshop
-
Register and attend
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Participate actively
Implement Vertex AI platform
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Implement and deploy machine learning models using Vertex AI's capabilities.
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Vertex AI
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Create a Vertex AI project
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Configure a dataset
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Train a model
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Deploy a model
Train with Vertex AI notebooks
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Gain hands-on experience training machine learning models with Vertex AI notebooks.
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Create a Vertex AI notebook
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Import data
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Train a model
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Evaluate the model
Build a machine learning model pipeline
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Design and implement a complete machine learning model pipeline to automate the process of data preparation, model training, and evaluation.
Browse courses on
Machine Learning
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Define the pipeline
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Configure the components
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Train the pipeline
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Evaluate the pipeline
Develop a machine learning application
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Apply machine learning techniques to solve a real-world problem through an application.
Browse courses on
Machine Learning
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-
Identify a problem
-
Gather data
-
Train a model
-
Deploy the application
Organize course materials
Show steps
Improve your understanding and retention of course materials by organizing them effectively.
Show steps
Show steps
Show steps
- Review notes and assignments
- Create summaries
- Organize materials digitally
Read "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
Show steps
Gain a solid understanding of machine learning concepts and techniques.
View
Aprende Machine Learning con Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Work through the exercises
Review linear algebra
Show steps
Strengthen your foundational understanding of linear algebra, a crucial skill for machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review textbooks
- Solve practice problems
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow tutorials on Vertex AI
Show steps
Enhance your understanding of Vertex AI's features and functionalities through hands-on tutorials.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Find relevant tutorials
- Follow the instructions
Attend a Vertex AI workshop
Show steps
Acquire practical knowledge and guidance from experts by attending a Vertex AI workshop.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Find a relevant workshop
- Register and attend
- Participate actively
Implement Vertex AI platform
Show steps
Implement and deploy machine learning models using Vertex AI's capabilities.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Create a Vertex AI project
- Configure a dataset
- Train a model
- Deploy a model
Train with Vertex AI notebooks
Show steps
Gain hands-on experience training machine learning models with Vertex AI notebooks.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Create a Vertex AI notebook
- Import data
- Train a model
- Evaluate the model
Build a machine learning model pipeline
Show steps
Design and implement a complete machine learning model pipeline to automate the process of data preparation, model training, and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the pipeline
- Configure the components
- Train the pipeline
- Evaluate the pipeline
Develop a machine learning application
Show steps
Apply machine learning techniques to solve a real-world problem through an application.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem
- Gather data
- Train a model
- Deploy the application
Career center
Learners who complete How Google does Machine Learning en Español will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. They work closely with data scientists and software engineers to ensure that models are accurate, efficient, and scalable. This course provides a comprehensive overview of Google's approach to machine learning, including best practices for data preparation, model training, and deployment. It also covers advanced topics such as model interpretability and responsible AI. As such, this course can help you build the skills and knowledge you need to succeed as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. They work on a variety of projects, such as developing new products, improving customer service, and reducing costs. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as natural language processing and computer vision. As such, this course can help you build the skills and knowledge you need to succeed as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, such as developing new products, improving customer service, and reducing costs. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and deployment. It also covers advanced topics such as distributed training and model serving. As such, this course can help you build the skills and knowledge you need to succeed as a Software Engineer.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of customers. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as product roadmapping and customer segmentation. As such, this course can help you build the skills and knowledge you need to succeed as a Product Manager.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis and statistics to extract insights from data. They work on a variety of projects, such as identifying trends, forecasting demand, and improving customer service. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as natural language processing and computer vision. As such, this course can help you build the skills and knowledge you need to succeed as a Data Analyst.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to help organizations improve their operations. They work on a variety of projects, such as developing new products, improving customer service, and reducing costs. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as business process modeling and financial analysis. As such, this course can help you build the skills and knowledge you need to succeed as a Business Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement financial models. They work on a variety of projects, such as developing new trading strategies, pricing financial instruments, and managing risk. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as time series analysis and stochastic processes. As such, this course can help you build the skills and knowledge you need to succeed as a Quantitative Analyst.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of mathematics, statistics, and financial modeling to assess and manage risks. They work on a variety of projects, such as developing new risk management strategies, pricing insurance products, and managing regulatory compliance. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as risk modeling and portfolio optimization. As such, this course can help you build the skills and knowledge you need to succeed as a Risk Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve complex problems in a variety of industries. They work on a variety of projects, such as developing new supply chain strategies, improving customer service, and reducing costs. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as optimization and simulation. As such, this course can help you build the skills and knowledge you need to succeed as an Operations Research Analyst.
Consultant
Consultant
Consultants use their knowledge of business and technology to help organizations improve their operations. They work on a variety of projects, such as developing new products, improving customer service, and reducing costs. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as change management and organizational development. As such, this course can help you build the skills and knowledge you need to succeed as a Consultant.
Researcher
Researcher
Researchers use their knowledge of machine learning to develop new algorithms and techniques. They work on a variety of projects, such as developing new ways to improve the accuracy of machine learning models, making machine learning models more interpretable, and developing new applications for machine learning. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as natural language processing, computer vision, and reinforcement learning. As such, this course can help you build the skills and knowledge you need to succeed as a Researcher.
Entrepreneur
Entrepreneur
Entrepreneurs use their knowledge of business and technology to start and grow their own businesses. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as product development and marketing. As such, this course can help you build the skills and knowledge you need to succeed as an Entrepreneur.
Educator
Educator
Educators use their knowledge of machine learning to teach students about this emerging field. This course provides a strong foundation in machine learning, including topics such as data preparation, model training, and evaluation. It also covers advanced topics such as natural language processing, computer vision, and reinforcement learning. As such, this course can help you build the skills and knowledge you need to succeed as an Educator.
Policymaker
Policymaker
Policymakers use their knowledge of machine learning to develop and implement policies that govern the use of this technology. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as algorithmic bias and fairness. As such, this course can help you build the skills and knowledge you need to succeed as a Policymaker.
Nonprofit Leader
Nonprofit Leader
Nonprofit Leaders use their knowledge of machine learning to develop and implement programs that use this technology to solve social problems. This course provides a strong foundation in machine learning, including topics such as data analysis, model interpretability, and responsible AI. It also covers advanced topics such as natural language processing and computer vision. As such, this course can help you build the skills and knowledge you need to succeed as a Nonprofit Leader.
For more career information including salaries, visit:
OpenCourser.com/course/twf8gf/how
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
How Google does Machine Learning en Español.
Este libro ofrece una visión general del aprendizaje automático, cubriendo temas como la teoría del aprendizaje, los algoritmos de aprendizaje y las aplicaciones del aprendizaje automático.
Este libro proporciona una base sólida en la ingeniería de aprendizaje automático, cubriendo temas como el ciclo de vida de los proyectos de aprendizaje automático, la gestión de datos, la selección de modelos, la evaluación y la implementación.
Este libro se centra en el aprendizaje automático utilizando Python, cubriendo temas como la manipulación de datos, el modelado y la evaluación.
Este libro ofrece una introducción práctica al aprendizaje automático utilizando bibliotecas populares como Scikit-Learn, Keras y TensorFlow. Es útil para aquellos que buscan una guía práctica para construir y desplegar modelos de aprendizaje automático.
Este libro se centra en el aprendizaje profundo, que es un subcampo del aprendizaje automático que utiliza redes neuronales. Proporciona una base sólida para los conceptos y técnicas fundamentales del aprendizaje profundo.
Save
Este libro proporciona una introducción completa al aprendizaje profundo, cubriendo temas como las redes neuronales, el aprendizaje por refuerzo y el procesamiento del lenguaje natural.
Save
Este libro adopta un enfoque algorítmico del aprendizaje automático, proporcionando una comprensión detallada de los algoritmos de aprendizaje automático fundamentales.
Este libro se centra en la interpretabilidad del aprendizaje automático, que es la capacidad de comprender las predicciones de los modelos de aprendizaje automático. Proporciona técnicas y herramientas para hacer que los modelos de aprendizaje automático sean más transparentes y explicables.
Este libro adopta un enfoque probabilístico del aprendizaje automático, proporcionando una base teórica sólida para los conceptos y algoritmos fundamentales. Es útil para aquellos que buscan una comprensión más profunda del fundamento matemático del aprendizaje automático.
Este libro se centra en los métodos de aprendizaje automático para datos escasos, que es un conjunto de datos que contienen muchos ceros. Proporciona algoritmos y técnicas para manejar datos escasos de manera efectiva.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/twf8gf/how
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Effort
1 semana de estudio, de 8 a 10 horas por semana
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Español
Transcript
English
This course belongs to a
collection
called
Machine Learning with TensorFlow on Google Cloud en Español. It's comprised of five courses:
- Launching into Machine Learning en Español
- Intro to TensorFlow en Español
- Art and Science of Machine Learning en Español
- Feature Engineering en Español
- How Google does Machine Learning en Español (viewing)
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