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En este proyecto aplicado y práctico aprenderás a entrenar redes neuronales recurrentes (RNN y LSTM) y modelos de Prophet para predecir series temporales. Tanto las redes LSTM como Prophet son algunos de los modelos más avanzados para predecir valores futuros en base a series de tiempo. Por ello, te enseñaremos a como pre-procesar y preparar tus datos, a entrenar los modelos, a evaluarlos, a optimizarlos y a utilizarlos para predecir datos futuros.
Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
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OpenCourser.com/course/hnhqgf/series
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Para aplicar en proyectos propios
Enseña a preprocesar y preparar los datos para el entrenamiento de modelos
Diseñado para aprender a entrenar modelos de predicción de series temporales
Para entrenar modelos eficientes y utilizarlos en la predicción de valores futuros
Evalúa y optimiza los modelos entrenados
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Create your own learning path.
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Reviews summary
Predicción de series temporales con deep learning
Según los estudiantes, este curso ofrece una excelente oportunidad práctica para dominar la predicción de series temporales utilizando RNN, LSTM y Prophet. Los estudiantes valoran especialmente los proyectos finales y los laboratorios prácticos, que facilitan la aplicación de los conceptos teóricos. La mayoría elogia la claridad y las habilidades explicativas del instructor. Sin embargo, algunos advierten que el curso asume conocimientos previos en Python y Machine Learning, lo que puede ser un desafío para principiantes absolutos. Se destaca su relevancia para problemas reales y su enfoque práctico y directo al grano. Adicionalmente, se ha observado una mejora continua del curso a lo largo del tiempo, con la resolución de problemas pasados.
Se sugieren más ejemplos de aplicación en la industria.
"Me gustaría ver más ejemplos de uso en la industria."
El curso ha sido actualizado y mejorado con el tiempo.
"Lo tomé hace un año y me ayudó mucho en mi trabajo. Ha mejorado con el tiempo."
Las explicaciones del instructor son valoradas por su claridad.
"El instructor explica muy bien. Realmente he aprendido mucho sobre LSTM y Prophet."
"Impresionante. La parte de Prophet es muy clara y útil."
"El instructor es muy bueno."
El curso sobresale en la aplicación práctica de conceptos.
"Excelente curso, muy práctico. Los proyectos finales son lo mejor para aplicar los conceptos."
"Súper práctico y directo al grano. Exactamente lo que buscaba para aplicar RNN/LSTM a problemas reales de series temporales."
"Los laboratorios son muy completos y me ayudaron a consolidar lo aprendido. La teoría está bien cubierta y la práctica es su punto fuerte."
El ritmo puede ser rápido para algunos estudiantes.
"A veces la explicación es un poco rápida, pero con práctica se entiende."
El curso exige conocimientos previos de Python y ML.
"El curso asume un nivel básico en Python y ML, lo cual es justo."
"El contenido es interesante pero me costó seguirlo. Creo que requiere conocimientos previos más sólidos de los que se mencionan."
"Para principiantes absolutos puede ser complicado."
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 Series temporales con Deep Learning (RNN, LSTM) y Prophet with these
activities:
Organiza tus notas y recursos
Show steps
Mejora tu organización y eficiencia revisando y consolidando los materiales del curso.
Show steps
-
Revisa las notas de las clases y los materiales de lectura.
-
Organiza tus notas en carpetas temáticas.
-
Crea un sistema de archivo para los recursos en línea.
Sesiones de estudio en grupo
Show steps
Fortalece tu comprensión discutiendo los conceptos del curso con tus compañeros.
Show steps
-
Forma un grupo de estudio con otros estudiantes.
-
Reuníos periódicamente para repasar el material.
-
Resolvéd dudas y compartid perspectivas.
Ejercicios prácticos sobre Prophet
Show steps
Mejora tus habilidades prácticas en el uso de Prophet para predecir series temporales.
Browse courses on
Prophet
Show steps
-
Carga y prepara los datos de la serie temporal.
-
Crea un modelo Prophet y ajusta sus parámetros.
-
Evalúa y mejora el rendimiento del modelo.
Two other activities
Expand to see all activities and additional details
Show all five activities
Tutoriales sobre técnicas avanzadas de optimización
Show steps
Mejora tus habilidades en la optimización de modelos de predicción de series temporales.
Show steps
-
Explora diferentes técnicas de optimización como descenso de gradiente.
-
Aplica técnicas de optimización a los modelos LSTM y Prophet.
-
Compara y contrasta los resultados de optimización.
Proyecto: Predicción de series temporales
Show steps
Aplica los conceptos aprendidos para crear un modelo que prediga valores futuros en una serie temporal.
Browse courses on
Prophet
Show steps
-
Selecciona una serie temporal y define el objetivo de predicción.
-
Explora y prepara los datos.
-
Entrena y evalúa modelos LSTM y Prophet.
-
Presenta los resultados y las conclusiones.
Organiza tus notas y recursos
Show steps
Mejora tu organización y eficiencia revisando y consolidando los materiales del curso.
Show steps
Show steps
Show steps
- Revisa las notas de las clases y los materiales de lectura.
- Organiza tus notas en carpetas temáticas.
- Crea un sistema de archivo para los recursos en línea.
Sesiones de estudio en grupo
Show steps
Fortalece tu comprensión discutiendo los conceptos del curso con tus compañeros.
Show steps
Show steps
Show steps
- Forma un grupo de estudio con otros estudiantes.
- Reuníos periódicamente para repasar el material.
- Resolvéd dudas y compartid perspectivas.
Ejercicios prácticos sobre Prophet
Show steps
Mejora tus habilidades prácticas en el uso de Prophet para predecir series temporales.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Carga y prepara los datos de la serie temporal.
- Crea un modelo Prophet y ajusta sus parámetros.
- Evalúa y mejora el rendimiento del modelo.
Two other activities
Expand to see all activities and additional details
Show all five activities
Tutoriales sobre técnicas avanzadas de optimización
Show steps
Mejora tus habilidades en la optimización de modelos de predicción de series temporales.
Show steps
Show steps
Show steps
- Explora diferentes técnicas de optimización como descenso de gradiente.
- Aplica técnicas de optimización a los modelos LSTM y Prophet.
- Compara y contrasta los resultados de optimización.
Proyecto: Predicción de series temporales
Show steps
Aplica los conceptos aprendidos para crear un modelo que prediga valores futuros en una serie temporal.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Selecciona una serie temporal y define el objetivo de predicción.
- Explora y prepara los datos.
- Entrena y evalúa modelos LSTM y Prophet.
- Presenta los resultados y las conclusiones.
Career center
Learners who complete Series temporales con Deep Learning (RNN, LSTM) y Prophet will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics and machine learning to extract insights from data. This course will help you develop the skills needed to clean and prepare data, train models, and interpret results. These skills are essential for Data Analysts who want to be able to build predictive models and make data-driven decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. This course will help you develop the skills needed to train and evaluate machine learning models. These skills are essential for Machine Learning Engineers who want to be able to build models that can solve real-world problems.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, machine learning, and data analysis to solve business problems. This course will help you develop the skills needed to clean and prepare data, train models, and interpret results. These skills are essential for Data Scientists who want to be able to build predictive models and make data-driven decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to solve problems in the financial industry. This course will help you develop the skills needed to build and evaluate quantitative models. These skills are essential for Quantitative Analysts who want to be able to make data-driven decisions.
Business Analyst
Business Analyst
Business Analysts use data to make recommendations and improve business processes. This course will help you develop the skills needed to collect, analyze, and interpret data. These skills are essential for Business Analysts who want to be able to identify and solve business problems.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and trends. This course will help you develop the skills needed to collect, analyze, and interpret data. These skills are essential for Market Researchers who want to be able to identify and target new markets.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematics and statistics to solve problems in the operations and logistics industries. This course will help you develop the skills needed to build and evaluate mathematical models. These skills are essential for Operations Research Analysts who want to be able to optimize systems and processes.
Risk Analyst
Risk Analyst
Risk Analysts use mathematics and statistics to assess and mitigate risks. This course will help you develop the skills needed to build and evaluate risk models. These skills are essential for Risk Analysts who want to be able to identify and manage risks.
Statistician
Statistician
Statisticians use mathematics and statistics to collect, analyze, and interpret data. This course will help you develop the skills needed to design and conduct statistical studies. These skills are essential for Statisticians who want to be able to draw conclusions from data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will help you develop the skills needed to implement machine learning models in software systems. These skills are essential for Software Engineers who want to be able to build software that can solve real-world problems.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course will help you develop the skills needed to clean and prepare data for machine learning models. These skills are essential for Data Engineers who want to be able to build data pipelines that can support machine learning applications.
Product Manager
Product Manager
Product Managers develop and manage products. This course will help you develop the skills needed to understand the needs of users and develop products that meet those needs. These skills are essential for Product Managers who want to be able to build products that are successful in the marketplace.
Project Manager
Project Manager
Project Managers plan and execute projects. This course will help you develop the skills needed to manage projects that involve machine learning. These skills are essential for Project Managers who want to be able to deliver successful machine learning projects.
Consultant
Consultant
Consultants help organizations solve problems. This course will help you develop the skills needed to understand the needs of clients and develop solutions that meet those needs. These skills are essential for Consultants who want to be able to help organizations solve problems using machine learning.
Teacher
Teacher
Teachers educate students. This course will help you develop the skills needed to teach students about machine learning. These skills are essential for Teachers who want to be able to prepare students for careers in machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/hnhqgf/series
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
Series temporales con Deep Learning (RNN, LSTM) y Prophet.
Save
Provides a comprehensive overview of time series analysis methods, with a focus on applications in R. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Save
Provides a comprehensive overview of the Box-Jenkins approach to time series analysis and forecasting. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Save
Provides a comprehensive overview of time series analysis methods, with a focus on a unified approach. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Provides a comprehensive overview of time series analysis methods, with a focus on theory. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Provides a comprehensive overview of time series analysis methods, with a focus on applications. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Save
Provides a comprehensive overview of time series models for business and economic forecasting. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
Provides a comprehensive overview of time series analysis methods, with a focus on R examples. It covers a wide range of topics, from data collection and preparation to model selection and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hnhqgf/series
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