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Leire Ahedo
En este proyecto aplicado y práctico aprenderás a entrenar modelos capaces de predecir series temporales. Para ello utilizaremos la librería de Pycaret con Python y entrenaremos modelos como: XGBoost, Catboost o Random forest. También aprenderemos a generar modelos más avanzados con lñas diferentes técnicas de ensamblado de modelos.
Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
Register for this course and see more details by visiting:
OpenCourser.com/course/8s9lhv/series
What's inside
Syllabus
Visión general del proyecto
En este proyecto aprenderemos a entrenar modelos de regresión adaptados a la predicción de series temporales con Python y Pycaret
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Desarrolla habilidades en la predicción de series temporales, lo que es muy relevante para la industria
Utiliza la biblioteca Pycaret, que es estándar en la industria
Enseña modelos avanzados con técnicas de ensamblado, lo que fortalece las habilidades de los estudiantes
Tiene una visión general del proyecto, lo que ayuda a los estudiantes a comprender el objetivo del curso
Requiere conocimientos previos en Python, lo que puede ser una barrera para algunos estudiantes
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Save Series Temporales con Pycaret y Python 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 Series Temporales con Pycaret y Python with these
activities:
Explorar librerías de regresión
Show steps
Familiarizarse con las funcionalidades y el uso de las librerías más populares para la predicción de series temporales.
Browse courses on
XGBoost
Show steps
-
Revisar y comprender la documentación oficial de Pycaret y XGBoost.
-
Seguir tutoriales y cursos en línea para aprender la aplicación práctica.
Ejercicios de práctica de predicción de series temporales
Show steps
Completar ejercicios de práctica ayudará a reforzar su comprensión de los conceptos y técnicas de predicción de series temporales.
Browse courses on
Pycaret
Show steps
-
Resolver ejercicios de práctica sobre diferentes tipos de series temporales (por ejemplo, estacionales, no estacionarias).
-
Experimentar con diferentes parámetros de modelo para optimizar el rendimiento.
-
Interpretar los resultados del modelo y sacar conclusiones sobre los patrones de datos.
Presentación sobre técnicas de predicción de series temporales
Show steps
Mejorar las habilidades de comunicación técnica y sintetizar los conocimientos adquiridos en el curso.
Show steps
-
Preparar diapositivas y contenido para la presentación.
-
Ensayar y practicar la presentación.
-
Presentar ante compañeros de clase o un público más amplio.
Show all three activities
Explorar librerías de regresión
Show steps
Familiarizarse con las funcionalidades y el uso de las librerías más populares para la predicción de series temporales.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- Revisar y comprender la documentación oficial de Pycaret y XGBoost.
- Seguir tutoriales y cursos en línea para aprender la aplicación práctica.
Ejercicios de práctica de predicción de series temporales
Show steps
Completar ejercicios de práctica ayudará a reforzar su comprensión de los conceptos y técnicas de predicción de series temporales.
Browse courses on
Pycaret
Show steps
Show steps
Show steps
- Resolver ejercicios de práctica sobre diferentes tipos de series temporales (por ejemplo, estacionales, no estacionarias).
- Experimentar con diferentes parámetros de modelo para optimizar el rendimiento.
- Interpretar los resultados del modelo y sacar conclusiones sobre los patrones de datos.
Presentación sobre técnicas de predicción de series temporales
Show steps
Mejorar las habilidades de comunicación técnica y sintetizar los conocimientos adquiridos en el curso.
Show steps
Show steps
Show steps
- Preparar diapositivas y contenido para la presentación.
- Ensayar y practicar la presentación.
- Presentar ante compañeros de clase o un público más amplio.
Show all three activities
Career center
Learners who complete Series Temporales con Pycaret y Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst uncovers hidden patterns and trends in data, helping businesses make better decisions. This course on time series prediction with Python and Pycaret provides a strong foundation for working with time-based data, which is essential for many data analysis tasks. By learning how to train and evaluate time series models, you can develop valuable skills for a career as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and maintains machine learning models. This course on time series prediction with Python and Pycaret introduces fundamental concepts of machine learning and provides hands-on experience with training and evaluating time series models. By completing this course, you can gain the skills needed to transition into a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods to extract knowledge and insights from data. This course on time series prediction with Python and Pycaret covers the essential skills for working with time series data, which is commonly encountered in many industries. By learning how to analyze and predict time series data, you can enhance your skills as a Data Scientist.
Financial Analyst
Financial Analyst
A Financial Analyst provides insights and recommendations on financial decisions. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast financial data. By understanding how to predict future trends, you can gain an advantage in your career as a Financial Analyst.
Business Analyst
Business Analyst
A Business Analyst identifies and solves business problems using data analysis. This course on time series prediction with Python and Pycaret equips you with the skills to analyze and forecast business metrics. By leveraging time series models, you can make data-driven decisions and contribute to the success of your organization as a Business Analyst.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course on time series prediction with Python and Pycaret provides a foundation in time series analysis, which is essential for many quantitative finance applications. By learning how to build and evaluate time series models, you can enhance your skills as a Quantitative Analyst.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course on time series prediction with Python and Pycaret covers the fundamentals of time series analysis, which is crucial for understanding and predicting insurance and financial risks. By gaining proficiency in time series modeling, you can advance your career as an Actuary.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to improve operational efficiency. This course on time series prediction with Python and Pycaret provides a foundation in time series analysis, which is applicable to various optimization problems. By learning how to analyze and forecast time series data, you can contribute to operational improvements as an Operations Research Analyst.
Market Researcher
Market Researcher
A Market Researcher gathers and analyzes data to understand market trends. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast market data. By gaining proficiency in time series modeling, you can make informed decisions and drive growth as a Market Researcher.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. This course on time series prediction with Python and Pycaret covers the essential concepts of time series analysis, which is a specialized field of statistics. By learning how to model and forecast time series data, you can enhance your skills as a Statistician.
Economist
Economist
An Economist analyzes and interprets economic data to make predictions about the economy. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast economic data. By gaining proficiency in time series modeling, you can make informed decisions and contribute to economic policy as an Economist.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of disease outbreaks. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast disease trends. By gaining proficiency in time series modeling, you can make informed decisions and contribute to public health as an Epidemiologist.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and manages risks to an organization. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast risks. By gaining proficiency in time series modeling, you can make informed decisions and contribute to the resilience of your organization as a Risk Manager.
Investment Analyst
Investment Analyst
An Investment Analyst studies and evaluates companies and their financial performance to make investment recommendations. This course on time series prediction with Python and Pycaret provides the tools and techniques needed to analyze and forecast financial data. By gaining proficiency in time series modeling, you can enhance your skills as an Investment Analyst.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data systems. This course on time series prediction with Python and Pycaret provides a strong foundation for working with time-based data, which is often encountered in data engineering tasks. By learning how to store, process, and analyze time series data, you can build robust and efficient data pipelines as a Data Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/8s9lhv/series
Reading list
We've selected ten 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 Pycaret y Python.
Save
Esta obra clásica proporciona una base teórica fundamental para el análisis y pronóstico de series temporales, complementando el enfoque práctico adoptado en este curso.
Save
Este libro clásico proporciona una base teórica integral sobre el análisis y pronóstico de series temporales, lo que lo convierte en una valiosa referencia para una comprensión más profunda de los conceptos subyacentes.
Save
Provides a comprehensive overview of time series analysis techniques, including both classical and modern approaches. It valuable resource for both beginners and experienced practitioners.
Save
Provides a practical guide to forecasting techniques, with a focus on real-world applications. It valuable resource for anyone who wants to learn how to forecast time series data.
Save
Este libro de texto avanzado ofrece una cobertura exhaustiva de las técnicas de análisis de series temporales, proporcionando una base sólida para una comprensión profunda.
Este libro de texto proporciona una cobertura integral de las técnicas econométricas para el análisis y pronóstico de series temporales, lo que lo convierte en un recurso valioso para aquellos interesados en las aplicaciones de series temporales en economía y finanzas.
Provides a comprehensive overview of time series analysis techniques, with a focus on R code. It valuable resource for anyone who wants to learn how to use R to analyze time series data.
Este libro explora el pronóstico bayesiano y los modelos dinámicos, proporcionando una comprensión de las técnicas avanzadas para el manejo de la incertidumbre en el pronóstico.
Provides a concise introduction to time series analysis. It valuable resource for anyone who wants to learn the basics of time series analysis.
Save
Este libro provee una introducción concisa al análisis de series temporales. Es un recurso valioso para estudiantes y profesionales que quieran aprender los conceptos básicos de este campo.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8s9lhv/series
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Effort
2 horas
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Desarrolla habilidades en la predicción de series temporales, lo que es muy relevante para la industria
Utiliza la biblioteca Pycaret, que es estándar en la industria
Enseña modelos avanzados con técnicas de ensamblado, lo que fortalece las habilidades de los estudiantes
Tiene una visión general del proyecto, lo que ayuda a los estudiantes a comprender el objetivo del curso
Requiere conocimientos previos en Python, lo que puede ser una barrera para algunos estudiantes
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