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Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de los problemas de regresión con Pycaret. Aprenderemos a generar un modelo predictivo de regresión capaz de predecir el valor de los diamantes. Para ello, aprenderemos, de manera práctica, a generar múltiples modelos de ML y metamodelos, a evaluar su eficiencia, a desplegarlos en producción y a guardarlos en MlFlow.
Register for this course and see more details by visiting:
OpenCourser.com/course/nf44d8/regresion
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What's inside
Syllabus
Regresión (ML) en la vida real con PyCaret
En este curso se aprenderá a utilizar Pycaret para generar modelos de regresión
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Prepara a los alumnos para utilizar Pycaret en problemas de regresión de la vida real
Proporciona una base para generar modelos predictivos de regresión
Enseña a implementar múltiples modelos de ML
Desarrolla habilidades para desplegar modelos en producción
Requiere conocimientos previos en regresión y Python
Save this course
Save Regresión (ML) en la vida real con PyCaret to your list so you can find it easily later:
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Reviews summary
Beginner-friendly pycaret course
This course is very well received and highly recommended for beginners seeking to learn PyCaret, with reviewers appreciating the project's practical and effective approach to learning regression problems. Reviewers also appreciate the excellent guides, which they rave as being exceptional.
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 Regresión (ML) en la vida real con PyCaret with these
activities:
Revisión de las habilidades básicas de Python
Show steps
Refrescar los conocimientos básicos de Python fortalecerá las bases para comprender los conceptos de regresión en el curso.
Browse courses on
Python
Show steps
-
Revisar los tipos de datos, variables y operadores
-
Practicar la manipulación de listas y diccionarios
-
Resolver ejercicios básicos de condicionales y bucles
Refresco de Conceptos Estadísticos
Show steps
Reforzar los fundamentos estadísticos necesarios para comprender los modelos de regresión.
Show steps
-
Revisar conceptos como media, mediana, desviación estándar y correlación.
-
Resolver problemas estadísticos básicos utilizando estos conceptos.
-
Practicar la interpretación de gráficos estadísticos, como histogramas y diagramas de dispersión.
-
Revisar las suposiciones subyacentes a los modelos de regresión.
Pre-course refresher on machine learning concepts
Show steps
Review fundamental concepts of supervised machine learning specifically focused on regression to brush up on the prerequisites for this course and maximize learning
Browse courses on
Machine Learning
Show steps
-
Revisit supervised learning techniques such as linear regression, polynomial regression, and decision trees
-
Gain a clear understanding of model evaluation metrics such as R-squared, RMSE, and MAE
-
Practice implementing regression models using NumPy and Scikit-Learn
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Attend a webinar or conference on regression techniques
Show steps
Expand knowledge and connect with experts in the field by attending industry events to stay abreast of advancements in regression techniques
Browse courses on
Networking
Show steps
-
Identify and register for upcoming webinars or conferences related to regression
-
Actively participate in sessions, ask questions, and engage with speakers
-
Connect with other attendees, exchange ideas, and explore potential collaborations
Discusión en Grupo sobre Evaluación de Modelos
Show steps
Mejorar la comprensión de las técnicas de evaluación de modelos mediante discusiones con compañeros.
Show steps
-
Formar un pequeño grupo de estudio con compañeros.
-
Seleccionar un conjunto de datos y un modelo de regresión para evaluar.
-
Utilizar diferentes métricas de evaluación para analizar el rendimiento del modelo.
-
Discutir los resultados de la evaluación y las implicaciones de cada métrica.
-
Presentar los hallazgos a la clase en una breve presentación.
Hands-on tutorial: Utilize PyCaret for regression tasks
Show steps
Follow guided tutorials that will provide hands-on experience with PyCaret for regression tasks, ensuring a practical understanding of the covered concepts
Browse courses on
Pycaret
Show steps
-
Set up a Python development environment and install PyCaret
-
Load and preprocess a dataset for regression
-
Train and evaluate multiple regression models using PyCaret
-
Visualize and interpret the results of the regression models
Crear un prototipo de modelo de regresión
Show steps
Mejorará la comprensión práctica de la creación de modelos de regresión.
Browse courses on
Machine Learning
Show steps
-
Definir los datos de entrada y salida del modelo
-
Seleccionar y aplicar algoritmos de regresión
-
Evaluar el rendimiento del modelo
-
Ajustar los parámetros del modelo para optimizar el rendimiento
Ejercicios Prácticos con Datos de Diamantes
Show steps
Fortalecer la comprensión de los modelos de regresión mediante la práctica de ejercicios con el conjunto de datos de diamantes.
Show steps
-
Descargar el conjunto de datos de diamantes de Kaggle.
-
Cargar el conjunto de datos en un cuaderno de Python.
-
Realizar la división del conjunto de datos en entrenamiento y prueba.
-
Crear y entrenar varios modelos de regresión utilizando Pycaret.
-
Evaluar el rendimiento de los modelos utilizando métricas como MSE y R2.
Comparación de Algoritmos de Regresión
Show steps
Ampliar la comprensión de los algoritmos de regresión mediante la comparación de su rendimiento en un conjunto de datos específico.
Show steps
-
Seleccionar un conjunto de datos de regresión y dividirlo en entrenamiento y prueba.
-
Elegir varios algoritmos de regresión, como Regresión Lineal, Árboles de Decisión y SVM.
-
Entrenar y evaluar cada algoritmo utilizando métricas como MSE y R2.
-
Analizar los resultados y comparar el rendimiento de los algoritmos.
-
Presentar los hallazgos en un informe o presentación.
Práctica de Hiperparametrización con Pycaret
Show steps
Mejorar la precisión de los modelos de regresión mediante la práctica de la hiperparametrización con Pycaret.
Show steps
-
Seleccionar un modelo de regresión específico, como Regresión Lineal o Arboles de Decisión.
-
Identificar los hiperparámetros clave del modelo.
-
Utilizar la función de cuadrícula de Pycaret para optimizar los hiperparámetros.
-
Evaluar los modelos optimizados utilizando métricas como MSE y R2.
-
Comparar el rendimiento de los modelos optimizados con los modelos no optimizados.
Solve regression coding challenges
Show steps
Attempt solving regression coding challenges to reinforce understanding of regression concepts and enhance problem-solving skills
Browse courses on
Regression
Show steps
-
Find online platforms or resources that provide regression coding challenges
-
Select a challenge and attempt to solve it
-
Compare solutions with others and discuss alternative approaches
Desarrollo de un Modelo de Predicción de Diamantes
Show steps
Aplicar las habilidades adquiridas en el curso para crear un modelo de regresión funcional para predecir el valor de los diamantes.
Show steps
-
Recopilar un conjunto de datos de diamantes con características relevantes.
-
Seleccionar y entrenar un modelo de regresión utilizando técnicas aprendidas en el curso.
-
Evaluar el rendimiento del modelo utilizando métricas como MSE y R2.
-
Desplegar el modelo en una plataforma de su elección (por ejemplo, Flask o Heroku).
-
Documentar el proceso y los resultados del proyecto en un informe técnico.
Develop a regression model for a real-world dataset
Show steps
Apply concepts learned in the course to develop a regression model for a real-world dataset, fostering practical implementation and critical thinking
Show steps
-
Identify a real-world dataset that aligns with your interests
-
Explore the dataset, understand the variables, and identify the target variable for regression
-
Train and evaluate different regression models using PyCaret
-
Present findings in a report or presentation, discussing the model's performance and potential applications
Revisión de las habilidades básicas de Python
Show steps
Refrescar los conocimientos básicos de Python fortalecerá las bases para comprender los conceptos de regresión en el curso.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisar los tipos de datos, variables y operadores
- Practicar la manipulación de listas y diccionarios
- Resolver ejercicios básicos de condicionales y bucles
Refresco de Conceptos Estadísticos
Show steps
Reforzar los fundamentos estadísticos necesarios para comprender los modelos de regresión.
Show steps
Show steps
Show steps
- Revisar conceptos como media, mediana, desviación estándar y correlación.
- Resolver problemas estadísticos básicos utilizando estos conceptos.
- Practicar la interpretación de gráficos estadísticos, como histogramas y diagramas de dispersión.
- Revisar las suposiciones subyacentes a los modelos de regresión.
Pre-course refresher on machine learning concepts
Show steps
Review fundamental concepts of supervised machine learning specifically focused on regression to brush up on the prerequisites for this course and maximize learning
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit supervised learning techniques such as linear regression, polynomial regression, and decision trees
- Gain a clear understanding of model evaluation metrics such as R-squared, RMSE, and MAE
- Practice implementing regression models using NumPy and Scikit-Learn
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Attend a webinar or conference on regression techniques
Show steps
Expand knowledge and connect with experts in the field by attending industry events to stay abreast of advancements in regression techniques
Browse courses on
Networking
Show steps
Show steps
Show steps
- Identify and register for upcoming webinars or conferences related to regression
- Actively participate in sessions, ask questions, and engage with speakers
- Connect with other attendees, exchange ideas, and explore potential collaborations
Discusión en Grupo sobre Evaluación de Modelos
Show steps
Mejorar la comprensión de las técnicas de evaluación de modelos mediante discusiones con compañeros.
Show steps
Show steps
Show steps
- Formar un pequeño grupo de estudio con compañeros.
- Seleccionar un conjunto de datos y un modelo de regresión para evaluar.
- Utilizar diferentes métricas de evaluación para analizar el rendimiento del modelo.
- Discutir los resultados de la evaluación y las implicaciones de cada métrica.
- Presentar los hallazgos a la clase en una breve presentación.
Hands-on tutorial: Utilize PyCaret for regression tasks
Show steps
Follow guided tutorials that will provide hands-on experience with PyCaret for regression tasks, ensuring a practical understanding of the covered concepts
Browse courses on
Pycaret
Show steps
Show steps
Show steps
- Set up a Python development environment and install PyCaret
- Load and preprocess a dataset for regression
- Train and evaluate multiple regression models using PyCaret
- Visualize and interpret the results of the regression models
Crear un prototipo de modelo de regresión
Show steps
Mejorará la comprensión práctica de la creación de modelos de regresión.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Definir los datos de entrada y salida del modelo
- Seleccionar y aplicar algoritmos de regresión
- Evaluar el rendimiento del modelo
- Ajustar los parámetros del modelo para optimizar el rendimiento
Ejercicios Prácticos con Datos de Diamantes
Show steps
Fortalecer la comprensión de los modelos de regresión mediante la práctica de ejercicios con el conjunto de datos de diamantes.
Show steps
Show steps
Show steps
- Descargar el conjunto de datos de diamantes de Kaggle.
- Cargar el conjunto de datos en un cuaderno de Python.
- Realizar la división del conjunto de datos en entrenamiento y prueba.
- Crear y entrenar varios modelos de regresión utilizando Pycaret.
- Evaluar el rendimiento de los modelos utilizando métricas como MSE y R2.
Comparación de Algoritmos de Regresión
Show steps
Ampliar la comprensión de los algoritmos de regresión mediante la comparación de su rendimiento en un conjunto de datos específico.
Show steps
Show steps
Show steps
- Seleccionar un conjunto de datos de regresión y dividirlo en entrenamiento y prueba.
- Elegir varios algoritmos de regresión, como Regresión Lineal, Árboles de Decisión y SVM.
- Entrenar y evaluar cada algoritmo utilizando métricas como MSE y R2.
- Analizar los resultados y comparar el rendimiento de los algoritmos.
- Presentar los hallazgos en un informe o presentación.
Práctica de Hiperparametrización con Pycaret
Show steps
Mejorar la precisión de los modelos de regresión mediante la práctica de la hiperparametrización con Pycaret.
Show steps
Show steps
Show steps
- Seleccionar un modelo de regresión específico, como Regresión Lineal o Arboles de Decisión.
- Identificar los hiperparámetros clave del modelo.
- Utilizar la función de cuadrícula de Pycaret para optimizar los hiperparámetros.
- Evaluar los modelos optimizados utilizando métricas como MSE y R2.
- Comparar el rendimiento de los modelos optimizados con los modelos no optimizados.
Solve regression coding challenges
Show steps
Attempt solving regression coding challenges to reinforce understanding of regression concepts and enhance problem-solving skills
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find online platforms or resources that provide regression coding challenges
- Select a challenge and attempt to solve it
- Compare solutions with others and discuss alternative approaches
Desarrollo de un Modelo de Predicción de Diamantes
Show steps
Aplicar las habilidades adquiridas en el curso para crear un modelo de regresión funcional para predecir el valor de los diamantes.
Show steps
Show steps
Show steps
- Recopilar un conjunto de datos de diamantes con características relevantes.
- Seleccionar y entrenar un modelo de regresión utilizando técnicas aprendidas en el curso.
- Evaluar el rendimiento del modelo utilizando métricas como MSE y R2.
- Desplegar el modelo en una plataforma de su elección (por ejemplo, Flask o Heroku).
- Documentar el proceso y los resultados del proyecto en un informe técnico.
Develop a regression model for a real-world dataset
Show steps
Apply concepts learned in the course to develop a regression model for a real-world dataset, fostering practical implementation and critical thinking
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with your interests
- Explore the dataset, understand the variables, and identify the target variable for regression
- Train and evaluate different regression models using PyCaret
- Present findings in a report or presentation, discussing the model's performance and potential applications
Career center
Learners who complete Regresión (ML) en la vida real con PyCaret will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use machine learning (ML) to solve business problems. This course teaches you how to use Pycaret, a popular ML library, to build regression models. Regression models are used to predict continuous values, such as the price of a house or the number of customers who will visit a store. By learning how to build regression models, you can gain the skills you need to become a successful data scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and deploy ML models. This course teaches you how to use Pycaret to build and deploy regression models. Regression models are used to predict continuous values, such as the price of a house or the number of customers who will visit a store. By learning how to build and deploy regression models, you can gain the skills you need to become a successful machine learning engineer.
Business Analyst
Business Analyst
Business analysts use data to improve business processes. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the number of customers who will visit a store or the amount of revenue a company will generate. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful business analyst.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use data to improve business operations. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the number of customers who will visit a store or the amount of revenue a company will generate. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful operations research analyst.
Statistician
Statistician
Statisticians use data to solve problems. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the price of a house or the number of customers who will visit a store. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful statistician.
Data Analyst
Data Analyst
Data analysts use data to solve business problems. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the price of a house or the number of customers who will visit a store. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful data analyst.
Financial Analyst
Financial Analyst
Financial analysts use data to make investment decisions. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the price of a stock or the number of customers who will buy a product. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful financial analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use math and statistics to solve business problems. This course teaches you how to use Pycaret to build regression models. Regression models are used to predict continuous values, such as the price of a stock or the number of customers who will buy a product. By learning how to build regression models, you can gain the skills you need to become a successful quantitative analyst.
Actuary
Actuary
Actuaries use math and statistics to assess risk. This course teaches you how to use Pycaret to build regression models. Regression models are used to predict continuous values, such as the probability of an accident or the cost of a medical claim. By learning how to build regression models, you can gain the skills you need to become a successful actuary.
Risk Manager
Risk Manager
Risk managers use data to assess risk and develop mitigation plans. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the probability of an accident or the cost of a medical claim. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful risk manager.
Auditor
Auditor
Auditors use data to assess financial risk. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the probability of fraud or the amount of a loss. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful auditor.
Industrial Engineer
Industrial Engineer
Industrial engineers use data to improve manufacturing processes. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the number of defects in a product or the amount of time it takes to assemble a product. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful industrial engineer.
Insurance Analyst
Insurance Analyst
Insurance analysts use data to assess risk and set premiums. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the probability of an accident or the cost of a medical claim. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful insurance analyst.
Compliance Analyst
Compliance Analyst
Compliance analysts use data to ensure that organizations comply with laws and regulations. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the probability of a violation or the amount of a fine. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful compliance analyst.
Quality Control Engineer
Quality Control Engineer
Quality control engineers use data to ensure that products meet quality standards. This course teaches you how to use Pycaret to analyze data and build regression models. Regression models are used to predict continuous values, such as the number of defects in a product or the amount of time it takes to assemble a product. By learning how to analyze data and build regression models, you can gain the skills you need to become a successful quality control engineer.
For more career information including salaries, visit:
OpenCourser.com/course/nf44d8/regresion
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
Regresión (ML) en la vida real con PyCaret.
This classic textbook offers a comprehensive introduction to statistical learning methods, including regression techniques. It covers a wide range of topics, from data exploration to model assessment. While it is not focused on PyCaret, it provides a rigorous foundation for understanding the fundamentals of regression modeling.
Save
This comprehensive textbook covers a wide range of machine learning topics, including regression analysis. It provides a rigorous mathematical treatment of the subject and delves into advanced concepts. While not focused on PyCaret, it offers a deep understanding of the theoretical foundations of regression modeling.
Presents a balanced treatment of both Bayesian and frequentist statistical modeling. It covers regression techniques, model selection, and Bayesian inference. While not focused on PyCaret, it provides a comprehensive understanding of the strengths and limitations of different statistical approaches.
Delves into advanced regression modeling techniques, including generalized linear models, mixed models, and nonparametric regression. While it is not specific to PyCaret, it provides a deeper understanding of the theoretical and practical aspects of regression modeling.
This classic textbook provides a comprehensive introduction to Bayesian data analysis. It covers Bayesian inference, model checking, and case studies. While not specific to regression modeling, it offers a foundation for understanding Bayesian approaches to data analysis, which can complement the frequentist perspective covered in the course.
Save
Focuses on practical aspects of predictive modeling, including regression techniques. It covers model building, evaluation, and deployment. While not specific to PyCaret, it provides valuable insights into the real-world challenges and considerations when developing predictive models.
Introduces Bayesian statistical modeling, which is an alternative approach to regression analysis. It covers Bayesian inference, model diagnostics, and case studies. While not directly related to PyCaret, it provides a complementary perspective on regression modeling and expands the learner's knowledge.
Este libro en español es una guía práctica para aprender sobre el aprendizaje automático con Python. Cubre los conceptos básicos de la regresión, la selección de modelos y la evaluación del rendimiento. Si bien no aborda específicamente PyCaret, complementa el curso proporcionando una base sólida y ejemplos en español.
Offers a broader perspective on machine learning, including regression techniques. It covers supervised and unsupervised learning algorithms, model selection, and performance evaluation. While not focused solely on PyCaret, it provides a solid theoretical and practical foundation that complements the course.
This comprehensive book covers various machine learning libraries, including Scikit-Learn, Keras, and TensorFlow. It provides in-depth explanations of regression models, model selection, and evaluation techniques. While it does not focus on PyCaret specifically, it offers a wider perspective on the implementation of regression models in Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nf44d8/regresion
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Effort
2 horas
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Prepara a los alumnos para utilizar Pycaret en problemas de regresión de la vida real
Proporciona una base para generar modelos predictivos de regresión
Enseña a implementar múltiples modelos de ML
Desarrolla habilidades para desplegar modelos en producción
Requiere conocimientos previos en regresión y Python
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