How Google does Machine Learning en Español from Coursera

¿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.

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.

Module 1: Qué significa centrarse en la IA

En este módulo, se aborda la creación de una estrategia de datos en torno al aprendizaje automático.

Module 2: Cómo trabaja Google con el aprendizaje automático

En este módulo, se comparten los conocimientos organizacionales que Google ha adquirido a lo largo de los años.

Module 3: Desarrollo del aprendizaje automático con Vertex AI

Todo aprendizaje automático comienza con algún tipo de objetivo, ya sea un caso de uso empresarial o académico, o un propósito que se intenta lograr. En este módulo, se revisa el proceso para determinar si el modelo está listo para producción, luego de la fase de “prueba de concepto” o “experimentación”.

Module 4: Desarrollo del aprendizaje automático con Notebooks de Vertex

En este módulo, se exploran los notebooks administrados y los notebooks administrados por el usuario para el desarrollo del aprendizaje automático en Vertex AI.

Module 5: Prácticas recomendadas para implementar el aprendizaje automático en Vertex AI

En este módulo, se revisan las prácticas recomendadas para una serie de procesos de aprendizaje automático en Vertex AI.

Module 6: Desarrollo con IA responsable

En este módulo, se analiza por qué los sistemas de aprendizaje automático no son exactos de forma predeterminada, y algunos de los aspectos que hay que tener en cuenta a la hora de infundir el AA en los productos.

Module 7: Resumen

Este módulo es un resumen del curso Cómo trabaja Google con el aprendizaje automático.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Google's approach to machine learning

This course offers a comprehensive overview of Google's approach to machine learning. It highlights the importance of AI in business strategy, showcases real-world applications, and provides hands-on experience through interactive labs. The course covers the fundamentals of machine learning, including data preparation, model training, and deployment. It also emphasizes ethical considerations and responsible AI development.

The course covers ethical and responsible AI.

"Realmente satisfactorio! No me esperaba el acercamiento que se tuvo a las diferentes APIs de ML, aunado a el detalle que se dio a evitar el sesgo desde una etapa temprana siendo un curso introductorio."

The course discusses business applications of AI.

"Habilidades y perspectivas de negocio, como hacer escalables los productos, la importancia de conocer los datos..."

The course introduces Google Cloud Platform.

"diferentes tecnologías de Google de forma introductoria."

Hands-on labs provide practical experience.

"Me gusta los laboratorios. "

Some reviewers experienced technical issues.

"La herramienta de qlabs no funciona, hay que entrar a la versión en inglés."

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 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
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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Review textbooks
Solve practice problems

Six other activities

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Follow tutorials on Vertex AI

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Enhance your understanding of Vertex AI's features and functionalities through hands-on tutorials.

Browse courses on Vertex AI

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Find relevant tutorials
Follow the instructions

Attend a Vertex AI workshop

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Acquire practical knowledge and guidance from experts by attending a Vertex AI workshop.

Browse courses on Vertex AI

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Find a relevant workshop
Register and attend
Participate actively

Implement Vertex AI platform

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Implement and deploy machine learning models using Vertex AI's capabilities.

Browse courses on Vertex AI

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Create a Vertex AI project
Configure a dataset
Train a model
Deploy a model

Train with Vertex AI notebooks

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Gain hands-on experience training machine learning models with Vertex AI notebooks.

Browse courses on Vertex AI

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Create a Vertex AI notebook
Import data
Train a model
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
Configure the components
Train the pipeline
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

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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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