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Bhaskarjit Sarmah
In this 2-hour long project-based course, you will learn how to interpret or explain the output of tree based ensemble machine learning models. You will generate shapely values for all the features for each observations in the dataset. You will then learn to generate global and local explainability plots and then interpret it. You will learn how to create different shap plots for interpretability like - waterfall plot, force plot, decision plot etc. and also understand the use cases for each of these plots.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing...
Read more
In this 2-hour long project-based course, you will learn how to interpret or explain the output of tree based ensemble machine learning models. You will generate shapely values for all the features for each observations in the dataset. You will then learn to generate global and local explainability plots and then interpret it. You will learn how to create different shap plots for interpretability like - waterfall plot, force plot, decision plot etc. and also understand the use cases for each of these plots.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing...
Read more
In this 2-hour long project-based course, you will learn how to interpret or explain the output of tree based ensemble machine learning models. You will generate shapely values for all the features for each observations in the dataset. You will then learn to generate global and local explainability plots and then interpret it. You will learn how to create different shap plots for interpretability like - waterfall plot, force plot, decision plot etc. and also understand the use cases for each of these plots.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/zfazxv/explaining
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focused on real-world applications, this course teaches methods and tools highly relevant to industry practitioners
Instructors Bhaskarjit Sarmah have a strong reputation for their work in machine learning model interpretability
This course strengthens an existing foundation for intermediate learners by teaching advanced concepts in model interpretability
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Reviews summary
Educational but incomplete
This machine learning course teaches you how to explain and interpret SHAP values in tree-based ensemble models. Reviews suggest that the course is helpful, but could be improved by providing more detailed explanations and by covering additional topics such as LIME.
Course is useful for learning about SHAP values.
"This was useful."
Course lacks detail and explanation.
"This was useful. But, it is largely copied from other online material. I'd like to see more detail and explanation"
"This course should go more in depth. Before introducing SHAP plots, it is necessary how explain how it is calculated and that is lacking in the course."
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 Explaining Tree Based Models Using SHAP with these
activities:
Refresh Python Coding Skills
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Sharpen your Python skills to ensure a solid foundation for the course.
Browse courses on
Python Programming
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-
Review Python syntax, data structures, and functions.
-
Practice solving simple Python coding problems.
-
Complete online coding challenges or exercises.
Organize Course Materials for Effective Review
Show steps
Enhance your learning process by organizing and reviewing course materials effectively.
Show steps
-
Gather all course materials including notes, assignments, and quizzes.
-
Organize and categorize materials using a system that works for you.
Engage in Discussion Forums on Ensemble Learning
Show steps
Gain diverse perspectives and enhance your understanding through discussions with peers.
Browse courses on
Ensemble Learning
Show steps
-
Identify online forums or communities focused on ensemble learning.
-
Participate in discussions, ask questions, and share your insights.
Four other activities
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Show all seven activities
Follow Tutorials on Ensemble Learning
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Expand your understanding of ensemble learning by exploring tutorials and examples.
Browse courses on
Ensemble Learning
Show steps
-
Identify reputable sources for ensemble learning tutorials.
-
Follow tutorials to implement ensemble learning algorithms in Python.
Solve Ensemble Learning Coding Problems
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Strengthen your coding skills and apply ensemble learning concepts through practice.
Browse courses on
Python Programming
Show steps
-
Locate online platforms or textbooks with ensemble learning coding problems.
-
Solve coding problems to reinforce your understanding.
Create Visual Explanations of Ensemble Learning Concepts
Show steps
Solidify your comprehension by creating visual representations of ensemble learning concepts.
Browse courses on
Ensemble Learning
Show steps
-
Identify key concepts in ensemble learning.
-
Choose appropriate visualization techniques to represent the concepts.
-
Create visual explanations using tools like diagrams, charts, or videos.
Develop a Mini Project on Ensemble Learning
Show steps
Apply your learning through a hands-on project, solidifying your understanding of ensemble learning.
Browse courses on
Ensemble Learning
Show steps
-
Define a project scope and objectives related to ensemble learning.
-
Collect and prepare a dataset for your project.
-
Implement ensemble learning algorithms to solve the project problem.
-
Evaluate and interpret the results of your project.
Refresh Python Coding Skills
Show steps
Sharpen your Python skills to ensure a solid foundation for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review Python syntax, data structures, and functions.
- Practice solving simple Python coding problems.
- Complete online coding challenges or exercises.
Organize Course Materials for Effective Review
Show steps
Enhance your learning process by organizing and reviewing course materials effectively.
Show steps
Show steps
Show steps
- Gather all course materials including notes, assignments, and quizzes.
- Organize and categorize materials using a system that works for you.
Engage in Discussion Forums on Ensemble Learning
Show steps
Gain diverse perspectives and enhance your understanding through discussions with peers.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Identify online forums or communities focused on ensemble learning.
- Participate in discussions, ask questions, and share your insights.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Tutorials on Ensemble Learning
Show steps
Expand your understanding of ensemble learning by exploring tutorials and examples.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Identify reputable sources for ensemble learning tutorials.
- Follow tutorials to implement ensemble learning algorithms in Python.
Solve Ensemble Learning Coding Problems
Show steps
Strengthen your coding skills and apply ensemble learning concepts through practice.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Locate online platforms or textbooks with ensemble learning coding problems.
- Solve coding problems to reinforce your understanding.
Create Visual Explanations of Ensemble Learning Concepts
Show steps
Solidify your comprehension by creating visual representations of ensemble learning concepts.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Identify key concepts in ensemble learning.
- Choose appropriate visualization techniques to represent the concepts.
- Create visual explanations using tools like diagrams, charts, or videos.
Develop a Mini Project on Ensemble Learning
Show steps
Apply your learning through a hands-on project, solidifying your understanding of ensemble learning.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Define a project scope and objectives related to ensemble learning.
- Collect and prepare a dataset for your project.
- Implement ensemble learning algorithms to solve the project problem.
- Evaluate and interpret the results of your project.
Career center
Learners who complete Explaining Tree Based Models Using SHAP will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, develop and deploy machine learning models. This course on explaining tree based models using SHAP will provide you with a deeper understanding of machine learning models and how to interpret their results, which is a critical skill for machine learning engineers.
Data Analyst
Data Analyst
Data analysts collect, clean, analyze and interpret data to help businesses make informed decisions. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to interpret and explain complex data, which is essential for data analysts.
Data Scientist
Data Scientist
Data scientists study data using advanced analytics techniques to extract meaningful insights and help make informed business decisions. This course on explaining tree based models using SHAP can help you develop the skills and knowledge you need to build and interpret machine learning models, which are essential for data science.
Business Analyst
Business Analyst
Business analysts help businesses make better decisions by analyzing data and identifying opportunities for improvement. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to analyze data and communicate your findings, which is essential for business analysts.
Statistician
Statistician
Statisticians collect, analyze and interpret data to help businesses and organizations make informed decisions. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to analyze data and understand statistical models, which is essential for statisticians.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical models to solve business problems and improve efficiency. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to build and interpret machine learning models, which are increasingly being used in operations research.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to build and interpret machine learning models, which are increasingly being used in quantitative finance.
Financial Analyst
Financial Analyst
Financial analysts use data to make investment decisions. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to analyze data and communicate your findings, which is essential for financial analysts.
Marketing Analyst
Marketing Analyst
Marketing analysts use data to understand customer behavior and make marketing decisions. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to analyze data and communicate your findings, which is essential for marketing analysts.
Product Manager
Product Manager
Product managers are responsible for overseeing the development and launch of new products. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to understand and communicate the technical aspects of new products, which is essential for product managers.
Consultant
Consultant
Consultants help businesses solve problems and improve their performance. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to analyze data and communicate your findings, which is essential for consultants.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course on explaining tree based models using SHAP will provide you with the skills and knowledge you need to build and interpret machine learning models, which are increasingly being used in actuarial science.
Data Engineer
Data Engineer
Data engineers design and maintain data systems. This course on explaining tree based models using SHAP may be useful for data engineers who want to develop machine learning applications.
Software Engineer
Software Engineer
Software engineers design, develop and maintain software systems. This course on explaining tree based models using SHAP may be useful for software engineers who want to develop machine learning applications.
Computer Programmer
Computer Programmer
Computer programmers write and maintain computer programs. This course on explaining tree based models using SHAP may be useful for computer programmers who want to develop machine learning applications.
For more career information including salaries, visit:
OpenCourser.com/course/zfazxv/explaining
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
Explaining Tree Based Models Using SHAP.
Focuses specifically on SHAP (SHapley Additive Explanations), a powerful technique for explaining the predictions of machine learning models. It provides a thorough understanding of SHAP and its applications in various domains.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It covers a wide range of topics, including Bayesian inference, statistical models, and optimization techniques. It valuable resource for understanding the theoretical foundations of machine learning and gaining a deeper understanding of the field.
Introduces the principles and practices of interpretable machine learning, providing guidance on how to make machine learning models more transparent and understandable. It covers various interpretability techniques, including SHAP values, feature importance, and decision trees.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics from the basics of supervised and unsupervised learning to more advanced concepts such as Bayesian inference and Markov models. It valuable resource for understanding the theoretical foundations of machine learning and gaining a deeper understanding of the field.
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Provides a comprehensive overview of deep learning, covering a wide range of topics from the basics of neural networks to more advanced concepts such as convolutional neural networks and recurrent neural networks. It valuable resource for understanding the core concepts of deep learning and gaining a deeper understanding of the field.
Provides a comprehensive overview of machine learning concepts and algorithms, with a focus on practical implementation using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for understanding the fundamentals of machine learning and gaining hands-on experience with model development and evaluation.
Provides a comprehensive overview of machine learning using the Python programming language. It covers a wide range of topics, from the basics of data preprocessing and model fitting to more advanced concepts such as feature engineering and hyperparameter tuning. It valuable resource for learning how to apply machine learning techniques in practice.
Provides a practical guide to data science, covering a wide range of topics from the basics of data wrangling and exploratory data analysis to more advanced concepts such as machine learning and deep learning. It valuable resource for learning how to apply data science techniques in practice.
Provides a comprehensive overview of statistical learning, covering a wide range of topics from the basics of regression and classification to more advanced concepts such as support vector machines and ensemble methods. It valuable resource for understanding the theoretical foundations of machine learning and gaining a deeper understanding of the field.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical algorithms. It valuable resource for understanding the core concepts of machine learning and gaining a deeper understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zfazxv/explaining
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Bhaskarjit Sarmah
Good to know
Focused on real-world applications, this course teaches methods and tools highly relevant to industry practitioners
Instructors Bhaskarjit Sarmah have a strong reputation for their work in machine learning model interpretability
This course strengthens an existing foundation for intermediate learners by teaching advanced concepts in model interpretability
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