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Leire Ahedo
Proyecto aplicado y práctico para aprender a entrenar modelos de Machine Learning como: AR, MA, ARMA, ARIMA, autoARIMA, SARIMA y autoSARIMA para predecir series temporales con Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/o8mwqb/machine
What's inside
Syllabus
Visión general del proyecto
En este proyecto se aprenderá a entrenar diferentes modelos para predecir series temporales con Python
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
El proyecto permite que los estudiantes obtengan experiencia práctica en el entrenamiento de modelos de Machine Learning para la predicción de series temporales en Python
Está dirigido a estudiantes con conocimientos previos en Machine Learning y Python que buscan desarrollar sus habilidades en el modelado de series temporales
El proyecto es práctico y se enfoca en la aplicación de técnicas de Machine Learning en un contexto del mundo real
Los estudiantes aprenderán a entrenar una variedad de modelos de Machine Learning para series temporales, incluyendo AR, MA, ARMA, ARIMA, autoARIMA, SARIMA y autoSARIMA
El proyecto es impartido por instructores expertos en Machine Learning y modelado de series temporales
El proyecto está diseñado para ser flexible y permitir que los estudiantes aprendan a su propio ritmo
Save this course
Save Machine Learning para series temporales con ARIMA, SARIMA... to your list so you can find it easily later:
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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 para series temporales con ARIMA, SARIMA... with these
activities:
Sigue tutoriales sobre el uso de modelos de series temporales en Python
Show steps
Seguir tutoriales te proporcionará instrucciones paso a paso y ejemplos concretos sobre cómo utilizar modelos de series temporales en Python.
Browse courses on
Python
Show steps
-
Busca tutoriales sobre modelos de series temporales en Python.
-
Sigue los pasos descritos en los tutoriales.
-
Experimenta con diferentes modelos y parámetros.
-
Aplica los conocimientos adquiridos para resolver problemas de predicción de series temporales.
Resuelve ejercicios de series temporales
Show steps
Resolver ejercicios de series temporales te ayudará a practicar y reforzar los conceptos aprendidos en el curso.
Browse courses on
Machine Learning
Show steps
-
Identifica el tipo de serie temporal que se está analizando.
-
Selecciona el modelo de series temporales adecuado.
-
Entrena el modelo utilizando los datos proporcionados.
-
Evalúa el rendimiento del modelo utilizando métricas adecuadas.
Crea un proyecto que utilice modelos de series temporales para predecir un conjunto de datos del mundo real
Show steps
Crear un proyecto te permitirá aplicar tus habilidades y conocimientos para resolver un problema del mundo real relacionado con series temporales.
Browse courses on
Machine Learning
Show steps
-
Define el problema de predicción de series temporales que deseas resolver.
-
Recopila y prepara los datos de series temporales relevantes.
-
Selecciona y entrena el modelo de series temporales adecuado.
-
Evalúa el rendimiento del modelo utilizando métricas apropiadas.
-
Implementa el modelo para hacer predicciones y resolver el problema de predicción de series temporales.
Show all three activities
Sigue tutoriales sobre el uso de modelos de series temporales en Python
Show steps
Seguir tutoriales te proporcionará instrucciones paso a paso y ejemplos concretos sobre cómo utilizar modelos de series temporales en Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Busca tutoriales sobre modelos de series temporales en Python.
- Sigue los pasos descritos en los tutoriales.
- Experimenta con diferentes modelos y parámetros.
- Aplica los conocimientos adquiridos para resolver problemas de predicción de series temporales.
Resuelve ejercicios de series temporales
Show steps
Resolver ejercicios de series temporales te ayudará a practicar y reforzar los conceptos aprendidos en el curso.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identifica el tipo de serie temporal que se está analizando.
- Selecciona el modelo de series temporales adecuado.
- Entrena el modelo utilizando los datos proporcionados.
- Evalúa el rendimiento del modelo utilizando métricas adecuadas.
Crea un proyecto que utilice modelos de series temporales para predecir un conjunto de datos del mundo real
Show steps
Crear un proyecto te permitirá aplicar tus habilidades y conocimientos para resolver un problema del mundo real relacionado con series temporales.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define el problema de predicción de series temporales que deseas resolver.
- Recopila y prepara los datos de series temporales relevantes.
- Selecciona y entrena el modelo de series temporales adecuado.
- Evalúa el rendimiento del modelo utilizando métricas apropiadas.
- Implementa el modelo para hacer predicciones y resolver el problema de predicción de series temporales.
Show all three activities
Career center
Learners who complete Machine Learning para series temporales con ARIMA, SARIMA... will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course provides a strong foundation in time series forecasting, which is a critical skill for Data Scientists who work on problems such as demand forecasting, fraud detection, and anomaly detection. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and maintains machine learning models. This course provides a strong foundation in time series forecasting, which is a critical skill for Machine Learning Engineers who work on problems such as demand forecasting, fraud detection, and anomaly detection. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematics and statistics to solve financial problems. This course provides a strong foundation in time series forecasting, which is a critical skill for Quantitative Analysts who work on problems such as risk management, portfolio optimization, and trading strategies. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in the financial industry and are essential for building accurate and reliable forecasting systems.
Business Analyst
Business Analyst
A Business Analyst uses data to solve business problems. This course provides a strong foundation in time series forecasting, which is a critical skill for Business Analysts who work on problems such as demand forecasting, customer segmentation, and market research. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Statistician
Statistician
A Statistician uses data to solve problems. This course provides a strong foundation in time series forecasting, which is a critical skill for Statisticians who work on problems such as data analysis, forecasting, and statistical modeling. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course provides a strong foundation in time series forecasting, which is a critical skill for Financial Analysts who work on problems such as stock price forecasting, bond yield forecasting, and economic forecasting. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in the financial industry and are essential for building accurate and reliable forecasting systems.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to identify and manage risks. This course provides a strong foundation in time series forecasting, which is a critical skill for Risk Analysts who work on problems such as credit risk, operational risk, and market risk. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in the financial industry and are essential for building accurate and reliable forecasting systems.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. This course provides a strong foundation in time series forecasting, which is a critical skill for Data Engineers who work on problems such as data cleansing, data transformation, and data integration. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for Software Engineers who work on problems such as time series forecasting, anomaly detection, and predictive maintenance. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Market Research Analyst
Market Research Analyst
A Market Research Analyst conducts research to understand consumer behavior. This course may be useful for Market Research Analysts who work on problems such as market segmentation, customer satisfaction, and brand awareness. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve business problems. This course may be useful for Operations Research Analysts who work on problems such as supply chain management, inventory management, and scheduling. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Actuary
Actuary
An Actuary uses mathematics and statistics to assess risk. This course may be useful for Actuaries who work on problems such as life insurance, health insurance, and pensions. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in the insurance industry and are essential for building accurate and reliable forecasting systems.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps customers achieve success with a product or service. This course may be useful for Customer Success Managers who work with customers who use time series forecasting to make decisions. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Sales Manager
Sales Manager
A Sales Manager leads a team of salespeople to achieve sales goals. This course may be useful for Sales Managers who work with customers who use time series forecasting to make decisions. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
Marketing Manager
Marketing Manager
A Marketing Manager leads a team of marketers to achieve marketing goals. This course may be useful for Marketing Managers who work with customers who use time series forecasting to make decisions. The course covers a variety of time series forecasting models, including ARIMA, SARIMA, and autoSARIMA. These models are widely used in industry and are essential for building accurate and reliable forecasting systems.
For more career information including salaries, visit:
OpenCourser.com/course/o8mwqb/machine
Reading list
We've selected nine 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 para series temporales con ARIMA, SARIMA....
Save
Este libro clásico proporciona una base teórica sólida para el análisis de series temporales y cubre en profundidad los modelos ARIMA y SARIMA.
Este libro proporciona una base completa para comprender los fundamentos del aprendizaje automático, que es esencial para comprender los modelos de series temporales como ARIMA y SARIMA.
Save
Este libro proporciona una base matemática sólida para el modelado y pronóstico de series temporales, incluyendo los modelos ARIMA y SARIMA.
Este libro proporciona una introducción integral al aprendizaje estadístico, que incluye técnicas relevantes para el análisis de series temporales.
Este libro proporciona una introducción completa a los métodos de pronóstico, incluidas las técnicas ARIMA y SARIMA.
Este libro proporciona una introducción avanzada a los métodos de espacio de estado para el análisis de series temporales, que ofrecen una alternativa a los modelos ARIMA y SARIMA.
Este libro explora el uso de métodos bayesianos para el pronóstico de series temporales, lo que proporciona una perspectiva diferente a los métodos clásicos como ARIMA y SARIMA.
Save
Este libro proporciona una introducción integral al aprendizaje profundo, que complementa los métodos clásicos de series temporales como ARIMA y SARIMA.
Este libro proporciona una introducción a los modelos econométricos con variables dependientes cualitativas, que pueden ser relevantes para el análisis de series temporales.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o8mwqb/machine
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Effort
2 horas
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
El proyecto permite que los estudiantes obtengan experiencia práctica en el entrenamiento de modelos de Machine Learning para la predicción de series temporales en Python
Está dirigido a estudiantes con conocimientos previos en Machine Learning y Python que buscan desarrollar sus habilidades en el modelado de series temporales
El proyecto es práctico y se enfoca en la aplicación de técnicas de Machine Learning en un contexto del mundo real
Los estudiantes aprenderán a entrenar una variedad de modelos de Machine Learning para series temporales, incluyendo AR, MA, ARMA, ARIMA, autoARIMA, SARIMA y autoSARIMA
El proyecto es impartido por instructores expertos en Machine Learning y modelado de series temporales
El proyecto está diseñado para ser flexible y permitir que los estudiantes aprendan a su propio ritmo
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