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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
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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
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning Interpretable: SHAP, PDP y permutacion with these
activities:
Review Machine Learning fundamentals
Show steps
Review the essential concepts of Machine Learning, covering topics like supervised and unsupervised learning, regression, classification, and model evaluation.
Browse courses on
Machine Learning
Show steps
-
Review online resources such as articles, tutorials, and videos on Machine Learning basics.
-
Revisit textbooks or lecture notes from previous courses on Machine Learning.
-
Participate in online forums or discussion groups to engage with other learners and experts.
Mentor a junior student or peer in Machine Learning
Show steps
Strengthen your understanding of Machine Learning concepts by explaining them to others and providing guidance.
Browse courses on
Machine Learning
Show steps
-
Identify a junior student or peer who would benefit from your mentorship in Machine Learning.
-
Set up regular meetings or communication channels to provide support and guidance.
-
Review and discuss Machine Learning concepts, answer questions, and offer advice.
Complete practice problems and coding exercises
Show steps
Apply your understanding of Machine Learning techniques through hands-on practice, solving problems and implementing algorithms.
Browse courses on
Machine Learning
Show steps
-
Work through practice problems and coding exercises provided in the course materials.
-
Find additional practice problems and coding exercises online or in textbooks.
-
Participate in online coding challenges or competitions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow guided tutorials on specific Machine Learning techniques
Show steps
Deepen your understanding of specific Machine Learning techniques by following guided tutorials and experimenting with different approaches.
Browse courses on
Machine Learning
Show steps
-
Identify specific Machine Learning techniques you want to learn or improve upon.
-
Search for and follow guided tutorials that provide step-by-step instructions and examples.
-
Implement the techniques in your own projects or experiments.
Attend a workshop on interpretable Machine Learning techniques
Show steps
Enhance your knowledge and skills in interpretable Machine Learning by attending a workshop led by experts.
Browse courses on
Machine Learning
Show steps
-
Research and identify relevant workshops on interpretable Machine Learning techniques.
-
Register for and attend the workshop, actively participating in discussions and exercises.
-
Apply the knowledge and techniques learned in your own Machine Learning endeavors.
Read "Interpretable Machine Learning" by Christoph Molnar
Show steps
Gain a deeper understanding of interpretable Machine Learning techniques and their applications.
View
Interpretable Machine Learning
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting key concepts.
-
Discuss the book's contents with peers or a mentor to enhance understanding.
-
Apply the concepts to your own Machine Learning projects or research.
Participate in a Machine Learning competition that emphasizes interpretability
Show steps
Challenge yourself and test your skills in interpretable Machine Learning by participating in a competition.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a Machine Learning competition that focuses on interpretability.
-
Develop and implement interpretable Machine Learning models to solve the competition's problem.
-
Evaluate your models' performance and interpretability, making adjustments as needed.
-
Submit your final models and a report explaining your approach and findings.
Develop a Machine Learning project that demonstrates interpretability
Show steps
Apply your understanding of interpretable Machine Learning techniques by developing a project that demonstrates their practical applications.
Browse courses on
Machine Learning
Show steps
-
Define the problem you want to solve and gather the necessary data.
-
Choose appropriate interpretable Machine Learning techniques for your project.
-
Develop and implement your project, ensuring interpretability and explainability.
-
Present your project and findings to others, explaining the interpretable aspects of your model.
Review Machine Learning fundamentals
Show steps
Review the essential concepts of Machine Learning, covering topics like supervised and unsupervised learning, regression, classification, and model evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review online resources such as articles, tutorials, and videos on Machine Learning basics.
- Revisit textbooks or lecture notes from previous courses on Machine Learning.
- Participate in online forums or discussion groups to engage with other learners and experts.
Mentor a junior student or peer in Machine Learning
Show steps
Strengthen your understanding of Machine Learning concepts by explaining them to others and providing guidance.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a junior student or peer who would benefit from your mentorship in Machine Learning.
- Set up regular meetings or communication channels to provide support and guidance.
- Review and discuss Machine Learning concepts, answer questions, and offer advice.
Complete practice problems and coding exercises
Show steps
Apply your understanding of Machine Learning techniques through hands-on practice, solving problems and implementing algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Work through practice problems and coding exercises provided in the course materials.
- Find additional practice problems and coding exercises online or in textbooks.
- Participate in online coding challenges or competitions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow guided tutorials on specific Machine Learning techniques
Show steps
Deepen your understanding of specific Machine Learning techniques by following guided tutorials and experimenting with different approaches.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify specific Machine Learning techniques you want to learn or improve upon.
- Search for and follow guided tutorials that provide step-by-step instructions and examples.
- Implement the techniques in your own projects or experiments.
Attend a workshop on interpretable Machine Learning techniques
Show steps
Enhance your knowledge and skills in interpretable Machine Learning by attending a workshop led by experts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and identify relevant workshops on interpretable Machine Learning techniques.
- Register for and attend the workshop, actively participating in discussions and exercises.
- Apply the knowledge and techniques learned in your own Machine Learning endeavors.
Read "Interpretable Machine Learning" by Christoph Molnar
Show steps
Gain a deeper understanding of interpretable Machine Learning techniques and their applications.
View
Interpretable Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, taking notes and highlighting key concepts.
- Discuss the book's contents with peers or a mentor to enhance understanding.
- Apply the concepts to your own Machine Learning projects or research.
Participate in a Machine Learning competition that emphasizes interpretability
Show steps
Challenge yourself and test your skills in interpretable Machine Learning by participating in a competition.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a Machine Learning competition that focuses on interpretability.
- Develop and implement interpretable Machine Learning models to solve the competition's problem.
- Evaluate your models' performance and interpretability, making adjustments as needed.
- Submit your final models and a report explaining your approach and findings.
Develop a Machine Learning project that demonstrates interpretability
Show steps
Apply your understanding of interpretable Machine Learning techniques by developing a project that demonstrates their practical applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve and gather the necessary data.
- Choose appropriate interpretable Machine Learning techniques for your project.
- Develop and implement your project, ensuring interpretability and explainability.
- Present your project and findings to others, explaining the interpretable aspects of your model.
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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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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