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Nestor Nicolas Campos Rojas

En este proyecto, vamos a crear un modelo de visión computacional utilizando Azure Machine Learning, luego desplegarlo para finalmente poder consumirlo.

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Syllabus

Visión general del proyecto
En este proyecto, vamos a crear un modelo de visión computacional utilizando Azure Machine Learning, luego desplegarlo para finalmente poder consumirlo.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for beginners and those with prior machine learning knowledge
Involves creating, deploying, and consuming a machine learning model
Taught by Nestor Nicolas Campos Rojas, a recognized expert in machine learning
Provides hands-on practice with Azure Machine Learning
Covers practical applications of machine learning in industry

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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 Desplegando un modelo de visión computacional con Azure ML with these activities:
Recopilar y revisar notas y materiales
Recopilar y revisar notas, tareas, cuestionarios y exámenes le ayudará a refrescar su memoria y fortalecer su comprensión de los materiales del curso.
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  • Recopilar todas las notas, tareas y materiales acumulados durante el curso
  • Revisar y resumir los puntos clave de cada material
  • Organizar los materiales de una manera lógica y accesible
Refrescar las habilidades de programación relacionadas con la visión computacional
Refrescar sus habilidades de programación le ayudará a implementar y depurar modelos de visión computacional de manera efectiva.
Browse courses on OpenCV
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  • Revisar los conceptos básicos de programación
  • Practicar la resolución de problemas de programación
  • Construir pequeños proyectos de programación relacionados con la visión computacional
Seguir tutoriales guiados sobre Azure Machine Learning
Seguir tutoriales guiados sobre Azure Machine Learning le ayudará a familiarizarse con la plataforma y sus capacidades.
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  • Identificar los tutoriales relevantes en la documentación o sitios web de Microsoft
  • Seguir paso a paso las instrucciones de los tutoriales
  • Experimentar con el código y los ejemplos proporcionados
Three other activities
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Practicar ejercicios de visión computacional
Practicar ejercicios de visión computacional le ayudará a reforzar su comprensión de los conceptos y técnicas clave.
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  • Identificar los conceptos y técnicas clave de visión computacional
  • Encontrar ejercicios y problemas de práctica en línea o en libros de texto
  • Resolver los ejercicios y problemas de práctica
  • Revisar sus soluciones y aprender de sus errores
Participar en talleres de visión computacional
Asistir a talleres de visión computacional le brindará la oportunidad de aprender de expertos y obtener experiencia práctica.
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  • Investigar y encontrar talleres relevantes en su área
  • Registrarse e asistir a los talleres
  • Participar activamente en las sesiones y hacer preguntas
  • Conectar con otros participantes y expertos en el campo
Construir un proyecto de visión computacional
Construir un proyecto de visión computacional le permitirá aplicar sus habilidades y conocimientos a un problema del mundo real.
Show steps
  • Identificar un problema o necesidad que pueda abordarse con visión computacional
  • Diseñar y planificar su proyecto
  • Recopilar los datos y recursos necesarios
  • Construir y entrenar su modelo de visión computacional
  • Evaluar y mejorar su modelo

Career center

Learners who complete Desplegando un modelo de visión computacional con Azure ML will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers design, develop, and maintain computer vision systems. As computer vision models grow increasingly complex, you will need to know how to build, retrain, and deploy your models in the cloud. Taking this course will help you build foundational skills in how to deploy computer vision models to the cloud, including performance monitoring, managing metrics, and model versioning.
Machine Learning Engineer
Machine Learning Engineers build, deploy, and manage machine learning models. Computer Vision models are considered one of the more complex machine learning models to build and deploy. Taking this course will help you gain the confidence to be able to build, deploy, and manage computer vision models.
Software Engineer
Software Engineers build, deploy, and maintain computer software. This course can be particularly useful for Software Engineers that are transitioning to computer vision, or who work on deploying computer vision models.
Data Scientist
Data Scientists develop and maintain data pipelines, conduct exploratory data analysis, and build machine learning models. This course will help build a foundation for building, deploying, and managing computer vision models, and will be particularly useful in deployments where computer vision is one part of a larger machine learning or data science project.
Product Manager
Product Managers are responsible for the strategy, roadmap, and execution of software products. This course may be useful for Product Managers who are working on products that incorporate computer vision models.
Data Analyst
Data Analysts collect, analyze, and interpret data. Taking this course may be helpful for Data Analysts who are working with computer vision models, or who are transitioning to Data Science.
Business Analyst
Business Analysts use analytics to help visualize business processes and make strategic decisions. Taking this course may be useful for Business Analysts who are working with computer vision models, or who are transitioning to more technical Business Analyst roles.
Operations Research Analyst
Operations Research Analysts use analytics to help businesses make better decisions. Taking this course may be useful for Operations Research Analysts who are working with computer vision models, or who are transitioning to more technical Operations Research Analyst roles.
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to analyze data and make predictions. This course may be useful for Quantitative Analysts who are working with computer vision models, or who are transitioning to more technical Quantitative Analyst roles.
Sales Analyst
Sales Analysts use data analytics to help businesses understand and grow their sales. Taking this course may be useful for Sales Analysts who are working with computer vision models, or who are transitioning to more technical Sales Analyst roles.
Financial Analyst
Financial Analysts use data analytics to make financial decisions. Taking this course may be useful for Financial Analysts who are working with computer vision models, or who are transitioning to more technical Financial Analyst roles.
Market Researcher
Market Researchers collect and analyze data about markets and customers. Taking this course may be useful for Market Researchers who are working with computer vision models, or who are transitioning to more technical Market Researcher roles.
Risk Analyst
Risk Analysts use analytics to help businesses identify and manage risks. Taking this course may be useful for Risk Analysts who are working with computer vision models, or who are transitioning to more technical Risk Analyst roles.
Healthcare Analyst
Healthcare Analysts use data analytics to improve healthcare quality and efficiency. Taking this course may be useful for Healthcare Analysts who are working with computer vision models, or who are transitioning to more technical Healthcare Analyst roles.
Biostatistician
Biostatisticians use statistical methods to analyze biological data. Taking this course may be useful for Biostatisticians who are working with computer vision models, or who are transitioning to more technical Biostatistician roles.

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 Desplegando un modelo de visión computacional con Azure ML.
Este libro ofrece una introducción práctica al aprendizaje profundo para la visión computacional, con un enfoque en aplicaciones prácticas utilizando bibliotecas como Keras y TensorFlow.
Este libro de texto clásico proporciona una base sólida en los algoritmos y técnicas de visión computacional, sirviendo como una referencia fundamental para estudiantes e investigadores.
Este libro cubre los fundamentos de la visión computacional con Python, incluyendo técnicas de procesamiento de imágenes, aprendizaje automático y redes neuronales convolucionales.
Este libro de texto proporciona una introducción completa a la visión computacional, cubriendo temas fundamentales como la geometría de la imagen, la representación de objetos y el reconocimiento de patrones.
Este libro proporciona una visión general de las técnicas de aprendizaje automático utilizadas en la visión computacional, incluyendo conceptos fundamentales, algoritmos y aplicaciones prácticas.
Este libro de texto clásico cubre los fundamentos del reconocimiento de patrones y el aprendizaje automático, incluyendo métodos estadísticos, redes neuronales y modelos de aprendizaje profundo.
Este libro ofrece una visión completa de los modelos de visión computacional, el aprendizaje automático y las técnicas de inferencia, con un enfoque en aplicaciones prácticas.
Este libro de texto clásico proporciona una base sólida en el procesamiento de imágenes digitales, que sirve como un recurso valioso para comprender los fundamentos y técnicas utilizadas en la visión computacional.
Este libro explora los fundamentos probabilísticos del reconocimiento de patrones, proporcionando una comprensión de los modelos estadísticos utilizados en la visión computacional y el aprendizaje automático.

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