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Machine Learning Interpretable
interpretML y LIME
Leire Ahedo
Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning interpretables. Se explican en profundidad diferentes técnicas de interpretabilidad de modelos como: interpretML y LIME que nos permitirá entender el porqué de las predicciones.
Read more
Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning interpretables. Se explican en profundidad diferentes técnicas de interpretabilidad de modelos como: interpretML y LIME que nos permitirá entender el porqué de las predicciones.
Read more
Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning interpretables. Se explican en profundidad diferentes técnicas de interpretabilidad de modelos como: interpretML y LIME que nos permitirá entender el porqué de las predicciones.
Gracias a esto, aprenderás a entrenar modelos Glassbox que puedas entender el porqué de sus decisiones.
Register for this course and see more details by visiting:
OpenCourser.com/course/bi2r2j/machine
What's inside
Syllabus
Machine Learning Interpretable: LIME e InterpretML
En este curso se aprenderá a generar modelos de interpretables Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Este curso es ideal para quienes desean comprender profundamente los modelos de aprendizaje automático
El temario cubre las técnicas esenciales de interpretación de modelos, como LIME e interpretML, lo que lo hace adecuado para aquellos que buscan ampliar sus conocimientos en esta área
Los instructores, Leire Ahedo, no están explícitamente mencionados como expertos reconocidos en el campo de la interpretación de modelos de aprendizaje automático
El curso está diseñado para enseñar a los alumnos a desarrollar modelos de caja de vidrio, brindándoles una comprensión integral de las predicciones del modelo
La descripción del curso no menciona explícitamente si el curso requiere conocimientos previos o experiencia en aprendizaje automático
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Activities
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Machine Learning Interpretable: interpretML y LIME.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning Interpretable: interpretML y LIME will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases. Taking this course can help you develop the skills needed to succeed as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists analyze data to extract meaningful insights and build predictive models. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases. Taking this course can help you develop the skills needed to succeed as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve business problems. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and opportunities. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Sales Analyst
Sales Analyst
Sales Analysts analyze data to identify sales trends and opportunities. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Fraud Analyst
Fraud Analyst
Fraud Analysts analyze data to identify and prevent fraud. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Financial Analyst
Financial Analyst
Financial Analysts analyze data to make investment recommendations. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze data to understand customer behavior and marketing effectiveness. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Risk Analyst
Risk Analyst
Risk Analysts analyze data to identify and mitigate risks. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. interpretML and LIME are machine learning interpretability techniques that can help you understand how your models make predictions. This can be valuable for debugging models, explaining results to stakeholders, and identifying potential biases.
For more career information including salaries, visit:
OpenCourser.com/course/bi2r2j/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 Interpretable: interpretML y LIME.
Provides a comprehensive overview of interpretable machine learning techniques, including both LIME and interpretML. It valuable resource for anyone who wants to learn more about these techniques and how to use them to build interpretable models.
Provides a comprehensive overview of model explainability in machine learning. It valuable resource for anyone who wants to learn more about interpretable AI and how to use it to build interpretable models.
Provides a practical introduction to machine learning using Python. It includes a chapter on interpretability, which provides a good overview of the topic.
Save
Este libro proporciona una base sólida en los fundamentos estadísticos y de optimización del Machine Learning. Es una lectura valiosa para cualquiera que quiera profundizar su comprensión de los modelos de Machine Learning.
Este libro proporciona una base matemática sólida para el Machine Learning. Cubre una amplia gama de temas, desde álgebra lineal hasta cálculo y optimización.
Este libro proporciona una introducción completa a los métodos de aprendizaje automático con escasez. Es una lectura valiosa para cualquiera que quiera aprender más sobre este importante tema.
Este libro proporciona una guía práctica para el Machine Learning con R. Es una buena opción para los principiantes que quieren aprender a utilizar R para el Machine Learning.
Save
Este libro proporciona una introducción completa al aprendizaje profundo. Es una buena opción para los estudiantes que quieren aprender los conceptos básicos y las aplicaciones prácticas.
Este libro proporciona una introducción completa al aprendizaje por refuerzo. Es una buena opción para los estudiantes que quieren aprender los conceptos básicos y las aplicaciones prácticas.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bi2r2j/machine
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Effort
2 horas
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Este curso es ideal para quienes desean comprender profundamente los modelos de aprendizaje automático
El temario cubre las técnicas esenciales de interpretación de modelos, como LIME e interpretML, lo que lo hace adecuado para aquellos que buscan ampliar sus conocimientos en esta área
Los instructores, Leire Ahedo, no están explícitamente mencionados como expertos reconocidos en el campo de la interpretación de modelos de aprendizaje automático
El curso está diseñado para enseñar a los alumnos a desarrollar modelos de caja de vidrio, brindándoles una comprensión integral de las predicciones del modelo
La descripción del curso no menciona explícitamente si el curso requiere conocimientos previos o experiencia en aprendizaje automático
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