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La incorporación del aprendizaje automático en las canalizaciones de datos aumenta la capacidad de las empresas para extraer estadísticas de sus datos. En este curso, veremos numerosas formas de incluir el aprendizaje automático en las canalizaciones de datos de Google Cloud según el nivel de personalización que se necesite. Para una personalización escasa o nula, en el curso se aborda AutoML. Para obtener más capacidades de aprendizaje automático a medida, el curso presenta Notebooks y BigQuery Machine Learning (BigQuery ML). Además, en este curso se aborda cómo llevar a producción soluciones de aprendizaje automático con Kubeflow. Los estudiantes obtendrán experiencia práctica en la creación de modelos de aprendizaje automático en Google Cloud con Qwiklabs.
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OpenCourser.com/course/95a0jt/smart
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What's inside
Syllabus
Introducción
En este módulo, presentamos el curso y el temario
Introducción a Analytics y a la IA
Este módulo trata sobre las opciones de AA en Google Cloud
Read more
Syllabus
Introducción
En este módulo, presentamos el curso y el temario
Introducción a Analytics y a la IA
Este módulo trata sobre las opciones de AA en Google Cloud
Read more
API de modelos de AA creadas previamente para datos no estructurados
Este módulo se enfoca en el uso de API de AA previamente creadas
en sus datos no estructurados
Análisis de macrodatos con Notebooks
Este módulo abarca el uso de Notebooks
Canalizaciones de AA de producción con Kubeflow
En este módulo, se explica la creación de modelos de AA personalizados y se presentan Kubeflow y AI Hub
Creación de modelos personalizados con SQL en BigQuery ML
Este módulo se trata de BigQuery ML
Creación de modelos personalizados con AutoML
Resumen
Este módulo es un resumen de los temas abordados en el curso
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Desarrolla habilidades de aprendizaje automático de nivel básico, intermedio y avanzado
Abarca varias opciones de aprendizaje automático según las necesidades de personalización
Proporciona experiencia práctica a través de Qwiklabs
Incluye Kubeflow para la implementación de soluciones de aprendizaje automático en producción
El temario aborda aspectos introductorios y avanzados del aprendizaje automático
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Save Smart Analytics, Machine Learning, and AI on GCP en Español to your list so you can find it easily later:
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Reviews summary
Smart analytics, machine learning, and ai on gcp en español
This course is highly recommended for all data analytics and artificial intelligence enthusiasts. It provides a comprehensive overview of the options available in Google Cloud for incorporating machine learning into data pipelines, with a focus on AutoML for minimal customization and Notebooks and BigQuery Machine Learning (BigQuery ML) for more tailored machine learning capabilities. The course also covers how to bring machine learning solutions into production with Kubeflow. Students gain hands-on experience building machine learning models on Google Cloud with Qwiklabs.
Taught by experienced professionals.
"The course is taught by experienced professionals who provide valuable insights and practical tips."
Well-structured and easy to follow.
"The course is well-structured and easy to follow."
Covers practical applications of ML in data pipelines.
"In this course, we'll see numerous ways to include machine learning in Google Cloud data pipelines..."
Provides hands-on experience building ML models on Google Cloud.
"Students gain hands-on experience building machine learning models on Google Cloud with Qwiklabs."
Occasional technical issues with labs.
"Malo te quitan los crédito y no puedes realizar los LAB por problemas propios del curso."
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 Smart Analytics, Machine Learning, and AI on GCP en Español with these
activities:
Revise fundamental data analysis concepts
Show steps
Reinforces your understanding of the foundational principles used throughout the course.
Browse courses on
Exploratory Data Analysis
Show steps
-
Review the main concepts of probability and statistics
-
Practice data cleaning and preparation techniques
-
Experiment with exploratory data analysis methods
Refresh your Machine Learning skills
Show steps
Review fundamental Machine Learning concepts to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
-
Revisit textbooks or online resources on Machine Learning basics
-
Complete practice problems or exercises to reinforce your understanding
Explore Google Cloud Training tutorials
Show steps
Deepen your understanding of Google Cloud Platform by following guided tutorials and completing practical exercises.
Browse courses on
Google Cloud Platform
Show steps
-
Visit the Google Cloud Training website
-
Search for tutorials related to data analytics and machine learning
-
Follow the instructions and complete the exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on Google Cloud's ML APIs
Show steps
Provides hands-on experience with the tools and techniques used in the course.
Browse courses on
AutoML
Show steps
-
Identify the relevant API for your task
-
Follow step-by-step tutorials to integrate the API
-
Experiment with different API parameters
-
Troubleshoot any issues you encounter
Practice using Google Cloud's AutoML
Show steps
Gain hands-on experience with Google Cloud's AutoML to develop models without extensive coding.
Browse courses on
AutoML
Show steps
-
Create a free Google Cloud account
-
Follow the AutoML documentation or tutorials
-
Experiment with different datasets and learn how to evaluate model performance
Build an ML model using AutoML
Show steps
Provides practical experience in building and deploying ML models.
Browse courses on
AutoML
Show steps
-
Define the problem and gather data
-
Choose the appropriate AutoML algorithm
-
Train and evaluate the model
-
Deploy the model to production
Build a project using Kubeflow
Show steps
Demonstrate your proficiency in deploying and managing machine learning models in production by building a project with Kubeflow.
Browse courses on
Kubeflow
Show steps
-
Design a project that leverages machine learning
-
Set up a Kubeflow environment
-
Develop and deploy your machine learning model
Start a personal machine learning project
Show steps
Apply your skills by initiating a personal machine learning project that aligns with your interests.
Browse courses on
Machine Learning
Show steps
-
Choose and train a machine learning model
-
Identify a problem or opportunity that can be addressed with machine learning
-
Gather and prepare a dataset
-
Evaluate and refine your model
-
Deploy your model and track its performance
Revise fundamental data analysis concepts
Show steps
Reinforces your understanding of the foundational principles used throughout the course.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Review the main concepts of probability and statistics
- Practice data cleaning and preparation techniques
- Experiment with exploratory data analysis methods
Refresh your Machine Learning skills
Show steps
Review fundamental Machine Learning concepts to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit textbooks or online resources on Machine Learning basics
- Complete practice problems or exercises to reinforce your understanding
Explore Google Cloud Training tutorials
Show steps
Deepen your understanding of Google Cloud Platform by following guided tutorials and completing practical exercises.
Browse courses on
Google Cloud Platform
Show steps
Show steps
Show steps
- Visit the Google Cloud Training website
- Search for tutorials related to data analytics and machine learning
- Follow the instructions and complete the exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on Google Cloud's ML APIs
Show steps
Provides hands-on experience with the tools and techniques used in the course.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Identify the relevant API for your task
- Follow step-by-step tutorials to integrate the API
- Experiment with different API parameters
- Troubleshoot any issues you encounter
Practice using Google Cloud's AutoML
Show steps
Gain hands-on experience with Google Cloud's AutoML to develop models without extensive coding.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Create a free Google Cloud account
- Follow the AutoML documentation or tutorials
- Experiment with different datasets and learn how to evaluate model performance
Build an ML model using AutoML
Show steps
Provides practical experience in building and deploying ML models.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Define the problem and gather data
- Choose the appropriate AutoML algorithm
- Train and evaluate the model
- Deploy the model to production
Build a project using Kubeflow
Show steps
Demonstrate your proficiency in deploying and managing machine learning models in production by building a project with Kubeflow.
Browse courses on
Kubeflow
Show steps
Show steps
Show steps
- Design a project that leverages machine learning
- Set up a Kubeflow environment
- Develop and deploy your machine learning model
Start a personal machine learning project
Show steps
Apply your skills by initiating a personal machine learning project that aligns with your interests.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose and train a machine learning model
- Identify a problem or opportunity that can be addressed with machine learning
- Gather and prepare a dataset
- Evaluate and refine your model
- Deploy your model and track its performance
Career center
Learners who complete Smart Analytics, Machine Learning, and AI on GCP en Español will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
As a Data Engineer, your goal is to build, deploy, and maintain data pipelines. Among other things, you will often be tasked with automating and optimizing these pipelines. This course introduces you to various ways of incorporating Machine Learning into data engineering pipelines using Google Cloud. You can use this course to solve problems like cleaning dirty data, generating new features and insights, and leveraging existing models in your data pipelines. These new skills can help you stay competitive in this field.
Data Scientist
Data Scientist
As a Data Scientist, your job is to extract actionable insights from data. This typically involves collecting the data, exploring the data, building models, and visualizing what you find. With this course, you will build skills in BigQuery Machine Learning, AutoML, and Kubeflow. These skills can help you increase your impact and become a more valuable asset to your team.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for all things software. Depending on the company you work for, this may mean building anything from core infrastructure to responsive websites. Regardless of what you are building, the principles you will learn in this course may help you work with data you encounter on a daily basis. As a bonus, these skills may help you stand out in interviews.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will focus on the development and maintenance of Machine Learning models. This course covers a wide variety of Machine Learning methods. As such, you may find this course can help you expand your skillset or, if you are new to the field, help you gain footing.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for transforming raw data into insights. These insights will often then be used by downstream analysts to make critical business decisions. This course teaches you how to leverage Machine Learning to uncover insights and automate data analytics tasks. This can help you work more effectively and produce insights more quickly, which can only help your career.
Business Analyst
Business Analyst
As a Business Analyst, you will elicit and document the needs of a business. This often involves understanding what stakeholders want and then converting those needs to technical specifications. This course may be helpful to you as it can help you learn how to process unstructured data using pre-trained APIs. You can use these skills to generate business requirements from less structured sources like emails, meeting minutes, or even audio calls.
Information Security Analyst
Information Security Analyst
As an Information Security Analyst, you will be responsible for protecting an organization's data and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. As part of this, you may be tasked with analyzing data to find vulnerabilities and potential threats. The skills you learn in this course can help you enhance your analytical skills and learn about Machine Learning models that may be helpful in this role.
IT Manager
IT Manager
As an IT Manager, you will be responsible for planning, implementing, and managing an organization's IT infrastructure. This may include tasks like managing cloud services, networking, and cybersecurity. This course can help you build a foundation in Machine Learning and cloud computing. These skills can help you find creative solutions to business challenges and advance your career.
Product Manager
Product Manager
As a Product Manager, you will work with stakeholders to gather requirements and translate them into the development of new products and features. Some of the skills you will need to become a successful Product Manager include research and analytics. With this course, you will learn how to effectively process and analyze data and extract actionable insights. These skills can help you build a strong foundation for your career.
Project Manager
Project Manager
As a Project Manager, you will be responsible for coordinating and managing projects. Some of the skills you will need to become a successful Project Manager include planning, resource management, and risk management. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
Salesforce Developer
Salesforce Developer
As a Salesforce Developer, you will use the Salesforce platform to design and build applications. Some of the skills you will need to become a successful Salesforce Developer include programming, cloud computing, and database management. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
Quality Assurance Manager
Quality Assurance Manager
As a Quality Assurance Manager, you will be responsible for planning and executing quality assurance activities. Some of the skills you will need to become a successful Quality Assurance Manager include testing, problem solving, and data analysis. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
Human Resources Manager
Human Resources Manager
As a Human Resources Manager, you will be responsible for managing all aspects of human resources for an organization. Some of the skills you will need to become a successful Human Resources Manager include recruiting, employee relations, and workforce planning. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data and making recommendations. Some of the skills you will need to become a successful Financial Analyst include financial modeling, valuation, and risk management. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
Marketing Manager
Marketing Manager
As a Marketing Manager, you will be responsible for developing and executing marketing campaigns. Some of the skills you will need to become a successful Marketing Manager include market research, campaign planning, and performance analysis. This course can help you build a foundation in Machine Learning. These skills can help you develop solutions to any challenges you may encounter in your work.
For more career information including salaries, visit:
OpenCourser.com/course/95a0jt/smart
Reading list
We've selected eight 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
Smart Analytics, Machine Learning, and AI on GCP en Español.
Save
Este libro es una referencia integral sobre el aprendizaje profundo, que cubre los fundamentos teóricos y los algoritmos más recientes. Es una lectura esencial para cualquier persona interesada en comprender y utilizar el aprendizaje profundo.
Este libro ofrece una introducción completa al aprendizaje automático con Python, que cubre una amplia gama de algoritmos y técnicas. Proporciona una base sólida para comprender y utilizar el aprendizaje automático en proyectos del mundo real.
Este libro proporciona una guía práctica para el aprendizaje automático en Python. Cubre una amplia gama de temas, incluida la preparación de datos, la selección de modelos y la implementación. Es una buena opción para aquellos que desean aprender a utilizar el aprendizaje automático en proyectos prácticos.
Kaggle es una plataforma de aprendizaje automático que alberga una gran colección de cuadernos sobre una amplia gama de temas. Estos cuadernos pueden ser una fuente valiosa de información sobre cómo utilizar el aprendizaje automático para resolver problemas del mundo real.
Este libro enseña los fundamentos del análisis de datos con Python, que cubre la manipulación de datos, la visualización y el modelado. Ofrece una guía completa para utilizar las bibliotecas de Python para el análisis de datos.
Este libro proporciona una introducción práctica al aprendizaje automático en Python. Es una buena opción para aquellos que desean aprender a utilizar el aprendizaje automático en proyectos prácticos.
Este libro proporciona una introducción práctica a TensorFlow, una biblioteca de aprendizaje automático de código abierto. Es una buena opción para aquellos que desean aprender a utilizar TensorFlow para desarrollar aplicaciones de aprendizaje automático.
Save
Este libro explora las aplicaciones prácticas del aprendizaje automático en el mundo empresarial. Ofrece una guía para comprender y utilizar el aprendizaje automático para resolver problemas comerciales y tomar mejores decisiones.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/95a0jt/smart
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Español
Transcript
English
Good to know
Desarrolla habilidades de aprendizaje automático de nivel básico, intermedio y avanzado
Abarca varias opciones de aprendizaje automático según las necesidades de personalización
Proporciona experiencia práctica a través de Qwiklabs
Incluye Kubeflow para la implementación de soluciones de aprendizaje automático en producción
El temario aborda aspectos introductorios y avanzados del aprendizaje automático
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