We may earn an affiliate commission when you visit our partners.
Course image
Leire Ahedo

Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning en un entorno de Big Data con PySpark en proyectos sanitarios.

Te enseñaremos desde cero las bases de PySpark hasta las funciones más complejas. Y finalmente acabarás desarrollando un modelo completo y avanzado con Spark en Jupyter Notebooks.

Enroll now

What's inside

Syllabus

Machine Learning con Pyspark aplicado al campo sanitario
En este curso se aprenderá a generar modelos de Machine Learning con Spark (MLlib) del sector de salud

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Dirigido a profesionales sanitarios interesados en aplicar Machine Learning en el ámbito de la salud
Desarrolla modelos de Machine Learning avanzados con Spark en Jupyter Notebooks
Basado en el framework PySpark, ampliamente utilizado en la industria
Impartido por expertos en Machine Learning y análisis de datos en el sector sanitario
Requiere conocimientos previos en programación y Machine Learning
El curso no está actualizado con las últimas versiones de software

Save this course

Save Machine Learning con Pyspark aplicado al campo sanitario to your list so you can find it easily later:
Save

Reviews summary

Well-received health-care machine learning course

This course on Machine Learning with PySpark for health-care applications is well-received. Students appreciate the focus on practical applications and hands-on learning with Spark in Jupyter Notebooks. The course covers everything from the basics to advanced functions of PySpark. Overall, students recommend this course for those interested in health-sector Machine Learning.
Hands-on learning with Jupyter Notebooks
"...acabaras desarrollando un modelo completo y avanzado con Spark en Jupyter Notebooks..."
Covers both basics and advanced concepts
"...Te enseñaremos desde cero las bases de PySpark hasta las funciones más complejas..."

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 Machine Learning con Pyspark aplicado al campo sanitario with these activities:
Revisar conceptos de Machine Learning
Repasar los conceptos básicos de Machine Learning te ayudará a comprender mejor las técnicas y algoritmos que aprenderás en este curso.
Browse courses on Machine Learning
Show steps
  • Leer artículos o libros introductorios sobre Machine Learning
  • Completar ejercicios o tutoriales online sobre algoritmos de Machine Learning
Practicar ejercicios de programación con Spark
La práctica regular de programación en Spark te permitirá desarrollar fluidez y confianza en el uso de esta herramienta para el procesamiento de Big Data.
Browse courses on Apache Spark
Show steps
  • Resolver problemas de programación con Spark en sitios web como LeetCode o HackerRank
  • Participar en desafíos de programación organizados por la comunidad de Spark
Crear una presentación o documento técnico sobre una aplicación avanzada de Spark en el sector sanitario
Crear una presentación o documento técnico te permitirá sintetizar y compartir tus conocimientos sobre una aplicación avanzada de Spark en el sector sanitario.
Show steps
  • Investigar y recopilar información sobre una aplicación avanzada de Spark en el sector sanitario
  • Diseñar la presentación o documento técnico con una estructura clara y concisa
  • Presentar los resultados y conclusiones de manera efectiva
One other activity
Expand to see all activities and additional details
Show all four activities
Contribuir a proyectos de código abierto relacionados con Spark
Participar en proyectos de código abierto relacionados con Spark te permitirá profundizar tus conocimientos y colaborar con la comunidad.
Browse courses on Apache Spark
Show steps
  • Identificar proyectos de código abierto relacionados con Spark en plataformas como GitHub
  • Estudiar el código fuente y comprender su arquitectura
  • Contribuir con mejoras o nuevas funcionalidades al proyecto

Career center

Learners who complete Machine Learning con Pyspark aplicado al campo sanitario will develop knowledge and skills that may be useful to these careers:
Clinical Data Analyst
Clinical data analysts use data to help improve the quality and efficiency of clinical care. They may use their knowledge of clinical data and analytics techniques to identify trends and patterns in data. This course may be helpful to a Clinical Data Analyst because it will help build a foundation in using PySpark in a big data setting.
Health Data Analyst
Health data analysts use data to help improve the quality and efficiency of healthcare. They may use their knowledge of healthcare data and analytics techniques to identify trends and patterns in data. This course may be helpful to a Health Data Analyst because it will help build a foundation in using PySpark in a big data setting.
Health Informatics Specialist
Health informatics specialists use data to improve the quality and efficiency of healthcare. They may use their knowledge of health informatics tools and techniques to identify trends and patterns in data. This course may be helpful to a Health Informatics Specialist because it will help build a foundation in using PySpark in a big data setting.
Data Engineer
Data engineers build and maintain data pipelines that collect, clean, and store data. They may use their knowledge of data engineering tools and techniques to solve problems for businesses and organizations. This course may be helpful to a Data Engineer because it will help build a foundation in using PySpark in a big data setting.
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states and events in specified populations. They may use their knowledge of epidemiology and biostatistics to identify trends and patterns in data. This course may be helpful to an Epidemiologist because it will help build a foundation in using PySpark in a big data setting.
Biostatistician
Biostatisticians use statistical methods to solve problems in the life sciences. They may use their knowledge of biostatistics and data analysis to identify trends and patterns in data. This course may be helpful to a Biostatistician because it will help build a foundation in using PySpark in a big data setting.
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems. They use their knowledge of machine learning algorithms and data science to solve problems for businesses. This course may be helpful to a Machine Learning Engineer because it will help build a foundation in using PySpark in a big data setting.
Medical Researcher
Medical researchers conduct research to improve the prevention, diagnosis, and treatment of diseases. They may use their knowledge of medicine and research methods to identify trends and patterns in data. This course may be helpful to a Medical Researcher because it will help build a foundation in using PySpark in a big data setting.
Data Scientist
Data scientists work on teams to gather and analyze data to solve business problems. They may use their scientific knowledge to develop new methods for data processing, analysis, and modeling. This course may be helpful to a Data Scientist because it will help build a foundation in using PySpark in a big data setting.
Healthcare Consultant
Healthcare consultants advise healthcare providers on how to improve the quality and efficiency of care. They may use their knowledge of healthcare and business to identify trends and patterns in data. This course may be helpful to a Healthcare Consultant because it will help build a foundation in using PySpark in a big data setting.
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. They may use their knowledge of statistics to solve problems for businesses and organizations. This course may be helpful to a Statistician because it will help build a foundation in using PySpark in a big data setting.
Data Analyst
Data analysts collect, clean, and analyze data to help businesses make informed decisions. They may use their statistical and programming skills to uncover trends and patterns in data. This course may be helpful to a Data Analyst because it will help build a foundation in using PySpark in a big data setting.
Business Intelligence Analyst
Business intelligence analysts use data to help businesses make better decisions. They may use their knowledge of business intelligence tools and techniques to identify trends and patterns in data. This course may be helpful to a Business Intelligence Analyst because it will help build a foundation in using PySpark in a big data setting.
Database Administrator
Database administrators maintain and optimize databases. They may use their knowledge of database management systems to solve problems for businesses and organizations. This course may be helpful to a Database Administrator because it will help build a foundation in using PySpark in a big data setting.
Software Engineer
Software engineers design, develop, and maintain software systems. They may use their knowledge of programming languages and software engineering principles to solve problems for businesses and organizations. This course may be helpful to a Software Engineer because it will help build a foundation in using PySpark in a big data setting.

Reading list

We've selected six 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 Machine Learning con Pyspark aplicado al campo sanitario.
Este libro proporciona una introducción completa a Machine Learning con Apache Spark, cubriendo temas fundamentales como el procesamiento de datos, la selección de características y la evaluación de modelos.
Si bien este libro tiene un alcance más amplio que el Machine Learning, proporciona una visión general completa de la Inteligencia Artificial en el cuidado de la salud, lo que lo convierte en un recurso valioso para comprender las tendencias y aplicaciones emergentes.
Este libro proporciona una base sólida en el pensamiento estadístico, que es esencial para comprender y aplicar Machine Learning en entornos de investigación clínica.
Una guía para principiantes que proporciona una introducción completa a Machine Learning con Python, lo que lo convierte en un recurso útil para aquellos que buscan una base sólida en el campo.
Este libro ofrece un enfoque práctico para Machine Learning utilizando bibliotecas populares como Scikit-Learn, Keras y TensorFlow, lo que lo convierte en un recurso valioso para aquellos interesados en implementaciones prácticas.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Machine Learning con Pyspark aplicado al campo sanitario.
Machine Learning y Regresión con PySpark. Guía paso a paso
Most relevant
ML y Big Data con PySpark para la retención de clientes
Most relevant
Big Data: procesamiento y análisis
Most relevant
Fundamentos de la publicidad en redes sociales
Most relevant
Corrección, estilo y variaciones de la lengua española
Most relevant
¡Luces, celular y acción! Crea contenidos audiovisuales...
Most relevant
Educación emocional para el bienestar personal
Most relevant
Exploración centrada en el paciente
Most relevant
Marketing para emprendedores
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser