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Machine Learning Interpretable
SHAP, PDP y permutacion
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: SHAP, Partial Dependence Plot, Permutation importance, etc...
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: SHAP, Partial Dependence Plot, Permutation importance, etc...
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: SHAP, Partial Dependence Plot, Permutation importance, etc 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/uoit4l/machine
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores methods to understand how Machine Learning models make decisions
Introduces Partial Dependence Plots, Permutation Importance, and other important techniques
Taught by instructors with a strong reputation in the field of Machine Learning interpretability
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Reviews summary
Effective machine learning theory
Machine Learning Interpretable: SHAP, PDP y permutacion is a well received course for those who are experienced with machine learning and want to better understand the inner workings of their models. Students who took the time to review the course appreciated that it helped them to understand and interpret the results of their models.
Theory increases learning.
"Realmente me gustó el hecho de que no tuve que usar "código", parece aumentar el aprendizaje (una vez que las tareas se terminan realmente más tarde)"
Activities
Coming soon
We're preparing activities for
Machine Learning Interpretable: SHAP, PDP y permutacion.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning Interpretable: SHAP, PDP y permutacion will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and builds data analysis and machine learning models that solve real-world business problems. They use their knowledge of computer science and mathematics to develop and improve algorithms that can learn from data. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Machine Learning Engineers. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Data Analyst
Data Analyst
Data Analysts use their knowledge of mathematics, statistics, and computer science to collect, clean, and analyze data. They identify trends and patterns in data to help businesses make better decisions. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Data Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to solve real-world problems. They collect, clean, and analyze data to identify trends and patterns. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Data Scientists. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to help organizations improve their performance. They identify problems and opportunities, and develop solutions to improve efficiency and effectiveness. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Business Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Computer Scientist
Computer Scientist
Computer Scientists use their knowledge of computer science and mathematics to design and develop computer systems. They create algorithms and data structures to solve problems and improve efficiency. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Computer Scientists. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve complex business problems. They develop models to optimize efficiency and effectiveness. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Operations Research Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Statistician
Statistician
Statisticians use their knowledge of mathematics, statistics, and computer science to collect, analyze, and interpret data. They develop models to make predictions and draw conclusions. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Statisticians. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science and mathematics to create software that meets the needs of users. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Software Engineers. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that collect, clean, and analyze data. They use their knowledge of computer science and mathematics to develop and improve systems that can handle large amounts of data. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Data Engineers. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to analyze financial data. They develop models to predict market trends and make investment recommendations. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Quantitative Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and computer science to assess and manage risk. They develop models to predict the likelihood and impact of risks. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Actuaries. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of mathematics, statistics, and computer science to assess and manage risk. They develop models to predict the likelihood and impact of risks. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Risk Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their knowledge of statistics, marketing, and computer science to analyze data and develop marketing campaigns. They use their knowledge of machine learning to automate tasks and improve the effectiveness of marketing campaigns. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Marketing Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance, statistics, and computer science to analyze financial data and make investment recommendations. They use their knowledge of machine learning to automate tasks and improve the accuracy of their recommendations. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Financial Analysts. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
Product Manager
Product Manager
Product Managers use their knowledge of business and technology to develop and launch new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of users. This course will help you build a strong foundation in machine learning interpretability, which is an essential skill for Product Managers. By understanding why your models make the predictions they do, you can improve their accuracy and reliability.
For more career information including salaries, visit:
OpenCourser.com/course/uoit4l/machine
Reading list
We've selected six 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: SHAP, PDP y permutacion.
Complements the course by providing a practical guide to making black box models explainable. It covers methods such as SHAP, PDP, and permutation importance. It is valuable additional reading for gaining a deeper understanding of interpretability techniques.
Provides a hands-on guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone interested in using these libraries for machine learning.
Provides a practical guide to using PyTorch and Scikit-Learn for machine learning. It covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone interested in using these libraries for machine learning.
Provides a practical guide to machine learning using R. It covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone interested in using R for machine learning.
Provides a practical guide to deep learning using Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone interested in developing deep learning models using Python.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uoit4l/machine
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Explores methods to understand how Machine Learning models make decisions
Introduces Partial Dependence Plots, Permutation Importance, and other important techniques
Taught by instructors with a strong reputation in the field of Machine Learning interpretability
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