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Google Cloud Training

El curso comienza con un debate sobre los datos: cómo mejorar la calidad de los datos y cómo realizar análisis exploratorios de datos. Describimos Vertex AI AutoML y cómo compilar, entrenar y, luego, implementar un modelo de AA sin escribir ni una sola línea de código Conocerá los beneficios de BigQuery ML. Luego, se analiza cómo optimizar un modelo de aprendizaje automático (AA) y cómo la generalización y el muestreo pueden ayudar a evaluar la calidad de los modelos de AA para el entrenamiento personalizado.

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What's inside

Syllabus

Introducción
En este módulo, se brinda una descripción general del curso y sus objetivos.
Conozca sus datos: Mejore los datos por medio de análisis exploratorios de datos
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En este módulo, examinaremos cómo mejorar la calidad de los datos y explorar los datos mediante análisis exploratorios de datos. Observaremos la importancia de tener datos limpios y ordenados para el aprendizaje automático y mostraremos su efecto en la calidad de los datos. Por ejemplo, los valores omitidos pueden sesgar los resultados. También aprenderá la importancia de explorar sus datos. Una vez que los datos estén limpios y ordenados, realizaremos un análisis exploratorio del conjunto de datos.
El aprendizaje automático en la práctica
En este módulo, presentaremos algunos de los tipos principales de aprendizaje automático, de modo que pueda acelerar su crecimiento como profesional del AA.
Entrenamiento de modelos de AutoML con Vertex AI
En este módulo, haremos una introducción al entrenamiento de modelos de AutoML con Vertex AI.
Aprendizaje automático en BigQuery: Desarrolle modelos de AA en el lugar en el que se encuentran sus datos
En este módulo, haremos una introducción a BigQuery ML y sus capacidades.
Optimización
En este módulo, explicaremos cómo optimizar sus modelos de AA.
Generalización y muestreo
Llegó el momento de responder una pregunta un tanto extraña: ¿en qué situaciones es mejor no elegir el modelo de AA más exacto? Como dimos a entender en el último módulo sobre la optimización, el solo hecho de que un modelo tenga una métrica de pérdida de 0 para un conjunto de datos no significa que vaya a tener un buen rendimiento cuando se aplique a datos nuevos del mundo real. Aprenderá a crear conjuntos de datos de entrenamiento, evaluación y prueba repetibles y establecer comparativas de rendimiento.
Resumen
Este módulo es un resumen del curso Introducción al aprendizaje automático

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Este curso se sumerge profundamente en el mundo del aprendizaje automático (AA) y proporciona las bases necesarias tanto para principiantes como para aquellos que buscan fortalecer su base de conocimientos en AA
Se centra en los aspectos prácticos del AA, lo que lo hace particularmente valioso para quienes buscan aplicar sus conocimientos en escenarios del mundo real
Incluye instructores de Google Cloud Training, lo que garantiza contenido de alta calidad de expertos en la industria
Aborda el análisis exploratorio de datos, un paso crucial a menudo pasado por alto en el proceso de AA, lo que lo convierte en un curso integral
Introduce AutoML de Vertex AI, una herramienta valiosa para automatizar el proceso de construcción de modelos de AA, lo que lo hace accesible incluso para aquellos sin experiencia en codificación
Destaca la importancia de la optimización de modelos y la evaluación de su generalización, brindando a los alumnos una comprensión integral del ciclo de vida del desarrollo de modelos de AA

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Reviews summary

In-depth intro to machine learning in spanish

This course is a comprehensive introduction to machine learning in Spanish. It covers a wide range of topics, from data analysis and exploration to model training and optimization. The course is well-received by students, with many positive reviews highlighting the clear explanations, helpful labs, and valuable content. Overall, this course is a great option for anyone looking to learn more about machine learning in Spanish.
The course covers a lot of material.
"Realmente una de las introducciones mas completa que visto al Machine Learning"
"Excelente curso muy completo"
The labs are a great way to practice the concepts.
"Muy buenos los laboratorios"
"ademas permite practicar cuestionarios de preguntas en donde aprendes mucho mas"
The course is well-explained.
"Información clara y precisa para acompañar el aprendizaje"
"Muy claras las explicaciones"
The course assumes some prior knowledge.
"It assumes a lot of knowledge, It throws a lot of theory at you and expects you to learn something of it..."
Some of the labs have issues.
"Los labs, no funcionaban correctamente (error 403)"
"algunos de los ejercicios propuestos requerían utilizar bases de datos que ya no estaban disponibles"

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 Launching into Machine Learning en Español with these activities:
Participe en desafíos de aprendizaje automático
Participar en desafíos de aprendizaje automático le permitirá aplicar sus habilidades en entornos competitivos, mejorar su comprensión y mantenerse al día con las últimas tendencias.
Show steps
  • Identifique y únase a desafíos de aprendizaje automático en plataformas en línea
  • Lea y comprenda las reglas y métricas de evaluación del desafío
  • Desarrolle y envíe soluciones utilizando sus conocimientos de aprendizaje automático
  • Analice los resultados y aprenda de las soluciones de otros participantes
Revisión de conceptos estadísticos
Fortalezca su base en estadística para comprender mejor los conceptos de aprendizaje automático.
Show steps
  • Repasar los tipos de variables y escalas de medición
  • Practicar el cálculo de medidas descriptivas
  • Comprender diferentes distribuciones de probabilidad
  • Revisar los conceptos de muestreo y estimación
Review course syllabus
Establish a foundational understanding of the course content
Show steps
  • Read through the course syllabus
  • Identify the major sections and topics covered in the course
  • Estimate time commitment and plan study schedule
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Realizar análisis de datos exploratorios
Mejore su capacidad para identificar, limpiar y transformar datos para uso de aprendizaje automático.
Show steps
  • Leer el artículo "Introducción al análisis exploratorio de datos"
  • Importar un conjunto de datos y explorar sus características
  • Usar histogramas, diagramas de dispersión y diagramas de caja para visualizar los datos
  • Identificar valores atípicos y datos faltantes
  • Transformar los datos para mejorar su calidad
Grupos de estudio
Discuta los conceptos del curso, resuelva problemas y comparta conocimientos con sus compañeros.
Show steps
  • Unirse o formar un grupo de estudio
  • Planificar sesiones regulares
  • Discutir los materiales del curso, abordar preguntas
  • Trabajar en conjunto en proyectos o asignaciones
Tutoriales guiados sobre Vertex AI AutoML
Adquiera experiencia práctica en el entrenamiento y la implementación de modelos de aprendizaje automático utilizando Vertex AI AutoML.
Show steps
  • Ver el tutorial "Introducción a Vertex AI AutoML"
  • Seguir los pasos para entrenar un modelo de AutoML
  • Implementar el modelo en una aplicación o sitio web
  • Monitorear y evaluar el rendimiento del modelo
Practice data cleaning with Python
Gain hands-on experience and reinforce understanding of data cleaning concepts
Browse courses on Data Cleaning
Show steps
  • Find online tutorials on data cleaning with Python
  • Follow the tutorials and apply the techniques to real-world datasets
  • Review documentation and seek assistance from online forums or mentors
Experimente con AutoML
Explorar las capacidades y limitaciones de AutoML de Vertex AI a través de experimentos prácticos consolidará su comprensión de sus aplicaciones en situaciones del mundo real.
Browse courses on Vertex AI AutoML
Show steps
  • Seleccione un conjunto de datos y defina el problema del aprendizaje automático
  • Cree y entrene modelos de AutoML utilizando diferentes algoritmos
  • Evalúe y compare el rendimiento de los modelos
  • Optimice los modelos de AutoML para mejorar su precisión
Build a machine learning model using BigQuery ML
Gain practical experience and demonstrate mastery of BigQuery ML
Browse courses on BigQuery ML
Show steps
  • Choose a suitable dataset and define the modeling objective
  • Use BigQuery ML to create and train a machine learning model
  • Evaluate the model's performance and make necessary adjustments
  • Document the process and insights gained
Proyecto: Desarrollar un modelo de aprendizaje automático con BigQuery ML
Amplíe sus habilidades prácticas en el desarrollo y la implementación de modelos de aprendizaje automático utilizando BigQuery ML.
Show steps
  • Identificar un problema para resolver con aprendizaje automático
  • Recolectar y preparar el conjunto de datos en BigQuery
  • Entrenar y evaluar un modelo de aprendizaje automático con BigQuery ML
  • Crear un panel de control para visualizar y monitorear el rendimiento del modelo
  • Presentar sus hallazgos y el modelo a las partes interesadas
Apply optimization techniques to machine learning models
Develop proficiency in optimizing models and enhance learning outcomes
Browse courses on Optimization
Show steps
  • Identify optimization techniques covered in the course
  • Solve optimization problems related to machine learning models
  • Implement optimization techniques in Python or another programming language
  • Compare results and fine-tune models based on evaluation metrics

Career center

Learners who complete Launching into Machine Learning en Español will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their knowledge of mathematics, statistics, and computer science to build models that can learn from data and make predictions. This course provides a comprehensive introduction to machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Machine Learning Engineers who want to learn more about machine learning or who want to use Vertex AI in their work.
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They often use machine learning to build models that can learn from data and make predictions. This course provides a comprehensive introduction to machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Data Scientists who want to learn more about machine learning or who want to use BigQuery ML in their work.
Software Engineer
Software Engineers design, develop, and test software applications. They use their knowledge of computer science to create software that meets the needs of users. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Software Engineers who want to learn more about machine learning or who want to use Vertex AI in their work.
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to analyze financial data. They use this data to make predictions about the future performance of financial markets. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Quantitative Analysts who want to learn more about machine learning or who want to use BigQuery ML in their work.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Product Managers who want to learn more about machine learning or who want to use Vertex AI in their work.
Business Analyst
Business Analysts use their knowledge of business and technology to help organizations improve their operations. They often use data analysis to identify problems and opportunities. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Business Analysts who want to learn more about machine learning or who want to use BigQuery ML in their work.
Data Analyst
Data Analysts use their programming and analytical skills to clean, transform, and analyze data. They often use machine learning to find patterns and insights in large datasets. This course provides a strong foundation in machine learning, including a discussion of data quality and analysis. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Data Analysts who want to learn more about machine learning or who want to use BigQuery ML in their work.
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project goals, timelines, and budgets. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Project Managers who want to learn more about machine learning or who want to use Vertex AI in their work.
Market Research Analyst
Market Research Analysts use their knowledge of mathematics, statistics, and computer science to analyze market data. They work with businesses to identify and target new customers. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Market Research Analysts who want to learn more about machine learning or who want to use BigQuery ML in their work.
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve business problems. They work with businesses to develop and implement strategies that improve efficiency and profitability. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Operations Research Analysts who want to learn more about machine learning or who want to use Vertex AI in their work.
Statistician
Statisticians use their knowledge of mathematics and statistics to analyze data. They work with scientists, engineers, and other professionals to help them make informed decisions. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Statisticians who want to learn more about machine learning or who want to use BigQuery ML in their work.
Database Administrator
Database Administrators are responsible for the design, implementation, and maintenance of database systems. They work with users to ensure that data is stored and retrieved efficiently. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Database Administrators who want to learn more about machine learning or who want to use BigQuery ML in their work.
Financial Analyst
Financial Analysts use their knowledge of mathematics, statistics, and computer science to analyze financial data. They work with businesses to make investment decisions. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Financial Analysts who want to learn more about machine learning or who want to use BigQuery ML in their work.
Risk Analyst
Risk Analysts use their knowledge of mathematics, statistics, and computer science to assess and manage risk. They work with businesses to identify and mitigate risks that could impact their operations. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of Vertex AI, a cloud-based machine learning platform. This course may be useful for Risk Analysts who want to learn more about machine learning or who want to use Vertex AI in their work.
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course provides a strong foundation in machine learning, including a discussion of data quality, analysis, and optimization. It also covers the use of BigQuery ML, a cloud-based machine learning platform. This course may be useful for Data Engineers who want to learn more about machine learning or who want to use BigQuery ML in their work.

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 Launching into Machine Learning en Español.
Proporciona una introducción completa al aprendizaje automático, desde los conceptos básicos hasta algoritmos avanzados. Es especialmente útil como referencia introductoria o como lectura adicional para aquellos interesados en aprender los fundamentos del aprendizaje automático.
Este libro proporciona una guía práctica para aplicar el aprendizaje automático al análisis de datos. Cubre los fundamentos del aprendizaje automático y muestra cómo utilizarlo para extraer información de datos.
Un libro práctico que guía a los lectores a través de la implementación de algoritmos de aprendizaje automático en Python. Es una valiosa lectura complementaria para aquellos interesados en el aspecto práctico del aprendizaje automático.
Un libro integral que cubre los fundamentos teóricos y prácticos del aprendizaje profundo. Es una lectura avanzada recomendada para aquellos interesados en el aprendizaje profundo y sus aplicaciones en varios dominios.
Este libro ofrece una cobertura completa del aprendizaje profundo, incluida la teoría, los algoritmos y las aplicaciones. Es particularmente relevante para aquellos interesados en temas avanzados de aprendizaje automático.
Este libro ofrece una visión general integral del aprendizaje automático para una audiencia amplia. Es particularmente útil para aquellos que buscan una comprensión básica de los conceptos y aplicaciones del aprendizaje automático.

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