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Nestor Nicolas Campos Rojas
Al completar este proyecto de 1 hora de duración, entenderás y podrás desarrollar tus propios modelos de regresión (lineal y logístico) a partir de un conjunto de datos definidos, y optimizar los algoritmos de forma automática para encontrar los mejores parámetros para tus modelos.
Read more
Al completar este proyecto de 1 hora de duración, entenderás y podrás desarrollar tus propios modelos de regresión (lineal y logístico) a partir de un conjunto de datos definidos, y optimizar los algoritmos de forma automática para encontrar los mejores parámetros para tus modelos.
Read more
Al completar este proyecto de 1 hora de duración, entenderás y podrás desarrollar tus propios modelos de regresión (lineal y logístico) a partir de un conjunto de datos definidos, y optimizar los algoritmos de forma automática para encontrar los mejores parámetros para tus modelos.
También podrás entender los pasos necesarios antes de diseñar tus modelos, como analizar tus datos y hacer limpiezas de acuerdo a los tipos de datos y caso de uso.
Register for this course and see more details by visiting:
OpenCourser.com/course/37z4jp/introduccion
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What's inside
Syllabus
Proyecto de algoritmos de regresión
En este proyecto, tú podrás usar algoritmos de regresión con tus propios conjuntos de datos para implementar modelos que permitan hacer predicciones con nuevos datos. Además, podrás entender y validar los modelos para medir su efectividad, junto con seleccionar los mejores atributos y descartar las características que no aporten a mejorar los modelos.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Desarrolla y fortalece tus capacidades en modelos de regresión, una técnica fundamental para predecir resultados basados en datos
Te permite aprender y aplicar algoritmos de regresión lineal y logística, ampliando tus habilidades de modelado
Proporciona una comprensión integral del proceso de modelado de regresión, desde el análisis de datos hasta la optimización de modelos
Te guía en la selección efectiva de atributos y la eliminación de características irrelevantes, mejorando la precisión de tus modelos
Fortalece tu comprensión de los pasos previos a la construcción de modelos, como el análisis y la limpieza de datos
Save this course
Save Introducción a los algoritmos de regresión to your list so you can find it easily later:
Save
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Reviews summary
Regression algorithms primer
This course is a beginner-friendly introduction to regression algorithms. It covers the basics of regression, including how to build and validate models. Students appreciate the step-by-step approach and clear explanations. Some students wish there was more theory included.
Concepts explained clearly
"Muy buenas explicaciones"
Great for beginners
"Buen Curso si te inicias en esto, te explica paso a paso para llegar al resultado"
Could use more theory
"El proyecto bastante autocontenido, aunque me hubiera gustado que tuviea algo mas de teoria respecto a los algoritmos y sus párametros."
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 Introducción a los algoritmos de regresión with these
activities:
Revise your knowledge on linear and logistic regression
Show steps
Helps you refresh your understanding of regression algorithms, which are essential for this course.
Browse courses on
Linear Regression
Show steps
-
Review online resources to brush up on the key concepts of linear and logistic regression models
-
Look back on your previous notes, assignments, or course materials on regression analysis
-
Solve practice problems or take a quiz to test your comprehension of linear and logistic regression
Explore additional tutorials on regression modeling
Show steps
Provides you with hands-on practice and further insights into regression techniques.
Browse courses on
Regression Modeling
Show steps
-
Search for online video tutorials on regression modeling for beginners or intermediates
-
Find tutorials that cover topics such as model evaluation, feature selection, and hyperparameter tuning
-
Work through the tutorials and implement the concepts in your own practice projects
Join a study group or discussion forum on regression analysis
Show steps
Provides opportunities to engage with peers, share knowledge, and clarify concepts.
Browse courses on
Regression Analysis
Show steps
-
Search for online forums or study groups dedicated to regression analysis
-
Join the group and actively participate in discussions, asking questions and sharing your insights
-
Connect with other learners, form study groups, and collaborate on projects
Five other activities
Expand to see all activities and additional details
Show all eight activities
Curate a collection of resources on regression analysis
Show steps
Helps you organize and synthesize relevant information to enhance your knowledge.
Browse courses on
Regression Analysis
Show steps
-
Use online search engines and databases to gather resources on regression analysis, including articles, tutorials, videos, and datasets
-
Organize the resources into a structured format, such as a website, blog, or online document
-
Categorize and tag the resources based on topics, difficulty levels, and target audience
-
Promote your curated collection to other students and learners interested in regression analysis
Develop a regression model for a real-world dataset
Show steps
Enhances your practical skills in applying regression algorithms to real-world scenarios.
Browse courses on
Regression Modeling
Show steps
-
Identify a dataset that aligns with your interests or a specific problem you want to solve
-
Clean and prepare the data, ensuring it's suitable for regression analysis
-
Apply different regression algorithms to the data and compare their performance
-
Interpret and evaluate the results, identifying the most appropriate model for your dataset
-
Deploy your model to make predictions and gain insights from the data
Apply your regression skills to a social or environmental project
Show steps
Provides practical experience while making a positive impact on society.
Browse courses on
Regression Analysis
Show steps
-
Identify organizations or initiatives that leverage regression analysis to address social or environmental issues
-
Contact the organization and inquire about volunteer opportunities that align with your skills and interests
-
Develop regression models or provide analytical support to help the organization achieve its goals
-
Share your findings and insights to raise awareness and advocate for positive change
Write a blog post or article on a regression-related topic
Show steps
Helps you consolidate your understanding by explaining concepts to others.
Browse courses on
Regression Analysis
Show steps
-
Choose a specific topic within regression analysis that you're passionate about or have expertise in
-
Research the topic thoroughly, referring to credible sources and academic papers
-
Write a clear and engaging blog post or article that explains the topic in a way that's accessible to a broad audience
-
Share your blog post or article on social media or other platforms to reach a wider audience
Volunteer as a mentor or tutor for students learning regression
Show steps
Strengthens your understanding by teaching others and providing support.
Browse courses on
Regression Analysis
Show steps
-
Identify opportunities to mentor or tutor students in regression analysis, either through online platforms or local organizations
-
Prepare materials and lesson plans to support the learning of your mentees
-
Meet with your mentees regularly to provide guidance, answer their questions, and encourage their progress
Revise your knowledge on linear and logistic regression
Show steps
Helps you refresh your understanding of regression algorithms, which are essential for this course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Review online resources to brush up on the key concepts of linear and logistic regression models
- Look back on your previous notes, assignments, or course materials on regression analysis
- Solve practice problems or take a quiz to test your comprehension of linear and logistic regression
Explore additional tutorials on regression modeling
Show steps
Provides you with hands-on practice and further insights into regression techniques.
Browse courses on
Regression Modeling
Show steps
Show steps
Show steps
- Search for online video tutorials on regression modeling for beginners or intermediates
- Find tutorials that cover topics such as model evaluation, feature selection, and hyperparameter tuning
- Work through the tutorials and implement the concepts in your own practice projects
Join a study group or discussion forum on regression analysis
Show steps
Provides opportunities to engage with peers, share knowledge, and clarify concepts.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Search for online forums or study groups dedicated to regression analysis
- Join the group and actively participate in discussions, asking questions and sharing your insights
- Connect with other learners, form study groups, and collaborate on projects
Five other activities
Expand to see all activities and additional details
Show all eight activities
Curate a collection of resources on regression analysis
Show steps
Helps you organize and synthesize relevant information to enhance your knowledge.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Use online search engines and databases to gather resources on regression analysis, including articles, tutorials, videos, and datasets
- Organize the resources into a structured format, such as a website, blog, or online document
- Categorize and tag the resources based on topics, difficulty levels, and target audience
- Promote your curated collection to other students and learners interested in regression analysis
Develop a regression model for a real-world dataset
Show steps
Enhances your practical skills in applying regression algorithms to real-world scenarios.
Browse courses on
Regression Modeling
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests or a specific problem you want to solve
- Clean and prepare the data, ensuring it's suitable for regression analysis
- Apply different regression algorithms to the data and compare their performance
- Interpret and evaluate the results, identifying the most appropriate model for your dataset
- Deploy your model to make predictions and gain insights from the data
Apply your regression skills to a social or environmental project
Show steps
Provides practical experience while making a positive impact on society.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify organizations or initiatives that leverage regression analysis to address social or environmental issues
- Contact the organization and inquire about volunteer opportunities that align with your skills and interests
- Develop regression models or provide analytical support to help the organization achieve its goals
- Share your findings and insights to raise awareness and advocate for positive change
Write a blog post or article on a regression-related topic
Show steps
Helps you consolidate your understanding by explaining concepts to others.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Choose a specific topic within regression analysis that you're passionate about or have expertise in
- Research the topic thoroughly, referring to credible sources and academic papers
- Write a clear and engaging blog post or article that explains the topic in a way that's accessible to a broad audience
- Share your blog post or article on social media or other platforms to reach a wider audience
Volunteer as a mentor or tutor for students learning regression
Show steps
Strengthens your understanding by teaching others and providing support.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify opportunities to mentor or tutor students in regression analysis, either through online platforms or local organizations
- Prepare materials and lesson plans to support the learning of your mentees
- Meet with your mentees regularly to provide guidance, answer their questions, and encourage their progress
Career center
Learners who complete Introducción a los algoritmos de regresión will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for analyzing data to extract insights and develop predictive models. This course can help you build a foundation in regression algorithms, a key technique used in data science. By understanding how to develop and optimize regression models, you can enhance your ability to make accurate predictions and drive data-driven decisions, making you a more effective Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models to solve complex problems. This course can help you build a foundation in regression algorithms, a type of machine learning algorithm commonly used for prediction. By understanding how to develop and optimize regression models, you can enhance your ability to build and deploy effective machine learning solutions, making you a more successful Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you build a foundation in regression algorithms, a key technique used in data analysis. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and extract meaningful insights, making you a more effective Data Analyst.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying areas for improvement. This course can help you build a foundation in regression algorithms, a key technique used in business analysis. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and make recommendations for improving business outcomes, making you a more effective Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make investment recommendations. This course can help you build a foundation in regression algorithms, a key technique used in financial analysis. By understanding how to develop and optimize regression models, you can enhance your ability to analyze financial data and make more informed investment decisions, making you a more successful Financial Analyst.
Market Researcher
Market Researcher
Market Researchers are responsible for conducting research to identify and understand customer needs. This course can help you build a foundation in regression algorithms, a key technique used in market research. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and extract meaningful insights into customer behavior, making you a more effective Market Researcher.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for analyzing data to improve efficiency and productivity. This course can help you build a foundation in regression algorithms, a key technique used in operations research. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and make recommendations for improving operational outcomes, making you a more effective Operations Research Analyst.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for assessing and managing risk. This course can help you build a foundation in regression algorithms, a key technique used in risk analysis. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and make recommendations for managing risk, making you a more effective Risk Analyst.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. This course can help you build a foundation in regression algorithms, a key technique used in statistics. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and draw meaningful conclusions, making you a more effective Statistician.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risk. This course can help you build a foundation in regression algorithms, a key technique used in actuarial science. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and make recommendations for managing financial risk, making you a more effective Actuary.
Econometrician
Econometrician
Econometricians are responsible for applying statistical methods to economic data. This course can help you build a foundation in regression algorithms, a key technique used in econometrics. By understanding how to develop and optimize regression models, you can enhance your ability to analyze economic data and make more informed decisions, making you a more effective Econometrician.
Epidemiologist
Epidemiologist
Epidemiologists are responsible for investigating the causes and spread of diseases. This course can help you build a foundation in regression algorithms, a key technique used in epidemiology. By understanding how to develop and optimize regression models, you can enhance your ability to analyze data and make recommendations for preventing and controlling diseases, making you a more effective Epidemiologist.
Biostatistician
Biostatistician
Biostatisticians are responsible for applying statistical methods to biological data. This course can help you build a foundation in regression algorithms, a key technique used in biostatistics. By understanding how to develop and optimize regression models, you can enhance your ability to analyze biological data and make more informed decisions, making you a more effective Biostatistician.
Survey Researcher
Survey Researcher
Survey Researchers are responsible for designing and conducting surveys to collect data. This course can help you build a foundation in regression algorithms, a key technique used in survey research. By understanding how to develop and optimize regression models, you can enhance your ability to analyze survey data and make more informed decisions, making you a more effective Survey Researcher.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining data infrastructure. This course may be useful for you if you are interested in learning about the use of regression algorithms in data engineering. While this course does not focus specifically on data engineering, it can provide you with a foundation in regression algorithms, which can be applied to data engineering tasks such as data cleaning and transformation.
For more career information including salaries, visit:
OpenCourser.com/course/37z4jp/introduccion
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
Introducción a los algoritmos de regresión.
Este libro seminal cubre los fundamentos estadísticos y computacionales del aprendizaje automático, incluidos los algoritmos de regresión. Es una referencia invaluable para aquellos que buscan una comprensión más profunda de las teorías subyacentes.
Este libro de texto popular cubre los métodos de regresión y los modelos lineales generalizados en profundidad. Proporciona una base estadística sólida para el modelado de datos.
Save
Este libro autorizado proporciona una visión integral del aprendizaje profundo, que incluye métodos de regresión basados en redes neuronales. Ofrece una comprensión avanzada de los últimos avances en el campo.
Este libro especializado se centra en los métodos de regresión en bioestadística. Cubre técnicas avanzadas como el modelado de supervivencia y los modelos jerárquicos.
Este libro especializado se centra en la regresión en el contexto de aplicaciones actuariales y financieras. Proporciona una comprensión profunda de los métodos de regresión avanzados y su uso en el mundo real.
Este libro ofrece una introducción completa al aprendizaje automático con Python, que incluye una cobertura de los algoritmos de regresión. Proporciona una base sólida para los conceptos y técnicas fundamentales.
Save
Este libro ofrece una introducción accesible a los modelos lineales. Proporciona una comprensión intuitiva de los conceptos y métodos fundamentales de la regresión.
Este libro práctico guía a los lectores a través de la implementación de algoritmos de regresión utilizando populares bibliotecas de Python como Scikit-Learn, Keras y TensorFlow. Ofrece una valiosa experiencia práctica.
Save
Este libro práctico se centra en el uso de R para el análisis de regresión y ANOVA. Proporciona un enfoque práctico para los estudiantes que buscan aplicar estos métodos a sus propios conjuntos de datos.
Este libro ofrece una base sólida en el análisis de datos con R, cubriendo desde la importación y limpieza de datos hasta el modelado y visualización. Proporciona un contexto valioso para los algoritmos de regresión discutidos en el curso.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/37z4jp/introduccion
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Effort
55 minutos
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Nestor Nicolas Campos Rojas
Language
Español
Good to know
Desarrolla y fortalece tus capacidades en modelos de regresión, una técnica fundamental para predecir resultados basados en datos
Te permite aprender y aplicar algoritmos de regresión lineal y logística, ampliando tus habilidades de modelado
Proporciona una comprensión integral del proceso de modelado de regresión, desde el análisis de datos hasta la optimización de modelos
Te guía en la selección efectiva de atributos y la eliminación de características irrelevantes, mejorando la precisión de tus modelos
Fortalece tu comprensión de los pasos previos a la construcción de modelos, como el análisis y la limpieza de datos
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