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Nestor Nicolas Campos Rojas
En este proyecto de 1 hora, aprenderás a utilizar Amazon Sagemaker para crear y desplegar tus modelos de Machine Learning aprovechando toda la potencialidad de la nube.
Además, aprenderás a usar Sagemaker Studio para simplificar tu trabajo.
Register for this course and see more details by visiting:
OpenCourser.com/course/5rzq5m/disenando
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What's inside
Syllabus
Diseñando modelos de ML con Amazon Sagemaker
Al final de este proyecto, tú entenderás y aplicarás el servicio de Amazon SageMaker para crear y desplegar tus modelos de Machine Learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Diseño de modelos de aprendizaje automático con Amazon SageMaker
Despliegue de modelos de aprendizaje para empresas de producción
Aprendizaje sobre las herramientas y servicios en la nube para la creación de modelos de aprendizaje automático
El uso de Amazon SageMaker Studio para simplificar tu trabajo
Impartido por Nestor Nicolas Campos Rojas, experto en el campo de Aprendizaje automático
Este programa es apropiado para aquellos con conocimientos previos en aprendizaje automático y desean ampliar sus conocimientos en la nube de Amazon
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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 Diseñando modelos de ML con Amazon Sagemaker with these
activities:
Revise basic ML concepts
Show steps
Revisit basic ML concepts to strengthen your foundation for the course
Browse courses on
Machine Learning Basics
Show steps
-
Review supervised learning algorithms
-
Practice data preprocessing and feature engineering
-
Refresh your knowledge on model evaluation metrics
Follow online tutorials on SageMaker
Show steps
Enhance your proficiency in using SageMaker through guided tutorials
Show steps
-
Explore the official SageMaker documentation and tutorials
-
Follow video tutorials on YouTube or other online platforms
-
Complete hands-on labs or workshops provided by AWS
Solve ML practice problems
Show steps
Practice solving ML problems to solidify your understanding of the concepts
Show steps
-
Work through coding exercises on platforms like LeetCode or Kaggle
-
Participate in ML competitions or hackathons
-
Build small ML projects to apply your skills
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group or discussion forum
Show steps
Collaborate with peers to enhance your understanding and exchange ideas
Show steps
-
Find a study group or online forum dedicated to ML
-
Participate in discussions, ask questions, and share your insights
-
Collaborate on projects or assignments
Attend workshops or webinars on SageMaker
Show steps
Enhance your knowledge by attending workshops or webinars
Show steps
-
Identify relevant workshops or webinars
-
Register and participate in the events
-
Take notes and apply the insights gained
Create a resource list on ML using SageMaker
Show steps
Organize and share valuable resources to support your learning
Show steps
-
Collect useful articles, tutorials, and documentation
-
Categorize and organize the resources
-
Share your resource list with others
Build a simple ML model using SageMaker
Show steps
Apply your skills by building a practical ML model using SageMaker
Show steps
-
Define the problem and gather the necessary data
-
Choose appropriate ML algorithms and train your model
-
Evaluate and deploy your model using SageMaker
Participate in ML hackathons or competitions
Show steps
Challenge yourself by participating in ML competitions to test your skills
Show steps
-
Identify relevant competitions or hackathons
-
Form a team or work individually
-
Develop and submit your solutions
Share your ML knowledge by mentoring others
Show steps
Reinforce your understanding by teaching and assisting others
Show steps
-
Identify opportunities to mentor or tutor others
-
Prepare materials and lesson plans
-
Provide guidance and support to mentees
Revise basic ML concepts
Show steps
Revisit basic ML concepts to strengthen your foundation for the course
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Review supervised learning algorithms
- Practice data preprocessing and feature engineering
- Refresh your knowledge on model evaluation metrics
Follow online tutorials on SageMaker
Show steps
Enhance your proficiency in using SageMaker through guided tutorials
Show steps
Show steps
Show steps
- Explore the official SageMaker documentation and tutorials
- Follow video tutorials on YouTube or other online platforms
- Complete hands-on labs or workshops provided by AWS
Solve ML practice problems
Show steps
Practice solving ML problems to solidify your understanding of the concepts
Show steps
Show steps
Show steps
- Work through coding exercises on platforms like LeetCode or Kaggle
- Participate in ML competitions or hackathons
- Build small ML projects to apply your skills
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group or discussion forum
Show steps
Collaborate with peers to enhance your understanding and exchange ideas
Show steps
Show steps
Show steps
- Find a study group or online forum dedicated to ML
- Participate in discussions, ask questions, and share your insights
- Collaborate on projects or assignments
Attend workshops or webinars on SageMaker
Show steps
Enhance your knowledge by attending workshops or webinars
Show steps
Show steps
Show steps
- Identify relevant workshops or webinars
- Register and participate in the events
- Take notes and apply the insights gained
Create a resource list on ML using SageMaker
Show steps
Organize and share valuable resources to support your learning
Show steps
Show steps
Show steps
- Collect useful articles, tutorials, and documentation
- Categorize and organize the resources
- Share your resource list with others
Build a simple ML model using SageMaker
Show steps
Apply your skills by building a practical ML model using SageMaker
Show steps
Show steps
Show steps
- Define the problem and gather the necessary data
- Choose appropriate ML algorithms and train your model
- Evaluate and deploy your model using SageMaker
Participate in ML hackathons or competitions
Show steps
Challenge yourself by participating in ML competitions to test your skills
Show steps
Show steps
Show steps
- Identify relevant competitions or hackathons
- Form a team or work individually
- Develop and submit your solutions
Share your ML knowledge by mentoring others
Show steps
Reinforce your understanding by teaching and assisting others
Show steps
Show steps
Show steps
- Identify opportunities to mentor or tutor others
- Prepare materials and lesson plans
- Provide guidance and support to mentees
Career center
Learners who complete Diseñando modelos de ML con Amazon Sagemaker will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and evaluates machine learning models to solve complex problems. Amazon SageMaker is a powerful tool that helps automate much of the process of creating and deploying machine learning models, making it an essential tool for Machine Learning Engineers. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Data Scientist
Data Scientist
A Data Scientist uses data to extract insights and solve problems. Amazon SageMaker is a powerful tool that can help Data Scientists build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. Amazon SageMaker is a powerful tool that can help Software Engineers build and deploy machine learning models, which can be used to add intelligence to software applications. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Cloud Architect
Cloud Architect
A Cloud Architect designs and manages cloud computing systems. Amazon SageMaker is a cloud-based service that can help Cloud Architects build and deploy machine learning models. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to extract insights. Amazon SageMaker is a powerful tool that can help Data Analysts build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Business Analyst
Business Analyst
A Business Analyst uses data to help businesses make better decisions. Amazon SageMaker is a powerful tool that can help Business Analysts build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. Amazon SageMaker is a powerful tool that can help Financial Analysts build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Product Manager
Product Manager
A Product Manager manages the development and launch of new products. Amazon SageMaker is a powerful tool that can help Product Managers build and deploy machine learning models, which can be used to add intelligence to new products. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand market trends. Amazon SageMaker is a powerful tool that can help Market Researchers build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Risk Analyst
Risk Analyst
A Risk Analyst assesses and manages risk. Amazon SageMaker is a powerful tool that can help Risk Analysts build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to make investment decisions. Amazon SageMaker is a powerful tool that can help Quantitative Analysts build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk. Amazon SageMaker is a powerful tool that can help Actuaries build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data systems. Amazon SageMaker is a powerful tool that can help Data Engineers build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. Amazon SageMaker is a powerful tool that can help Statisticians build and deploy machine learning models, which can be used to automate tasks, identify patterns, and make predictions. This course will help you learn how to use Amazon SageMaker to build and deploy your own machine learning models, giving you the skills you need to succeed in this in-demand field.
Software Developer
Software Developer
A Software Developer designs, develops, and maintains software applications. Amazon SageMaker is a powerful tool that can help Software Developers build and deploy machine learning models, which can be used to add intelligence to software applications. This course may be useful for Software Developers who want to learn how to use Amazon SageMaker to build and deploy their own machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/5rzq5m/disenando
Reading list
We've selected seven 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
Diseñando modelos de ML con Amazon Sagemaker.
Este libro ofrece una cobertura integral de las técnicas de ML y las bibliotecas de Python, brindando información sobre la implementación práctica de modelos de ML en Amazon SageMaker.
Este libro proporciona una introducción accesible a los conceptos básicos de ciencia de datos y programación de Python, estableciendo una base sólida para comprender el desarrollo y la implementación de modelos de ML en Amazon SageMaker.
Este libro ofrece una guía práctica para implementar modelos de ML utilizando bibliotecas de Python, proporcionando conocimientos prácticos aplicables al desarrollo de modelos en Amazon SageMaker.
Este libro ofrece una visión general accesible de los conceptos de ML y la programación de Python, proporcionando una base para comprender los fundamentos de los modelos de ML en Amazon SageMaker.
Save
Este libro sirve como un texto completo sobre el aprendizaje profundo, proporcionando una base sólida para aquellos interesados en comprender los conceptos y técnicas avanzadas utilizados en los modelos de aprendizaje profundo en Amazon SageMaker.
Este libro ofrece una introducción integral al aprendizaje por refuerzo, proporcionando información sobre los conceptos y algoritmos fundamentales que son aplicables al desarrollo de modelos de aprendizaje por refuerzo en Amazon SageMaker.
Este libro proporciona una base sólida en optimización convexa, que es esencial para comprender el desarrollo y la implementación eficientes de modelos de ML en Amazon SageMaker.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5rzq5m/disenando
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Effort
1 hora
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Nestor Nicolas Campos Rojas
Language
Español
Good to know
Diseño de modelos de aprendizaje automático con Amazon SageMaker
Despliegue de modelos de aprendizaje para empresas de producción
Aprendizaje sobre las herramientas y servicios en la nube para la creación de modelos de aprendizaje automático
El uso de Amazon SageMaker Studio para simplificar tu trabajo
Impartido por Nestor Nicolas Campos Rojas, experto en el campo de Aprendizaje automático
Este programa es apropiado para aquellos con conocimientos previos en aprendizaje automático y desean ampliar sus conocimientos en la nube de Amazon
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