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Epaminondas Kapetanios
In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/lqnil6/interpretable
What's inside
Syllabus
Interpretable machine learning applications: Part 1
Gain insights into the feature importance of your prediction model. Getting to know which features and their values are most significant for the prediction model, will not only give further insights into the prediction model for machine learning modelers and developers, but also for the intended users of a machine learning application. Hence, in this project, you will learn how to go beyond the development and use of a machine learning (ML) application based on a regression classifier, by adding on explainability and interpretation aspects of the ML application.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for intermediate learners or aspiring professionals in data science and analytics who seek practical applications
Focuses on real-world applications, tailoring to individuals working with business problems that can leverage machine learning insights
Provides hands-on instruction in using industry-relevant tools and techniques for interpretable machine learning
Project-based learning approach allows learners to apply concepts directly to practical scenarios
Guided by instructors with expertise in the practical applications of machine learning in industry
May require further knowledge in programming and statistical concepts for optimal comprehension
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Reviews summary
Hands-on interpretable machine learning
Students say this highly hands-on course about interpretable machine learning is well structured and provides engaging assignments. It's a great choice for learners looking for a course with an informative and to-the-point approach to the subject matter.
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 Interpretable Machine Learning Applications: Part 1 with these
activities:
Review machine learning basics
Show steps
Revisit the fundamental concepts of machine learning to strengthen your foundation before diving into the course material.
Browse courses on
Machine Learning
Show steps
-
Re-read introductory chapters from a machine learning textbook or online resource.
-
Practice solving basic machine learning problems using a programming language you are familiar with.
-
Review common machine learning algorithms and their applications.
Explore tutorials on interpretable machine learning
Show steps
Seek out tutorials to gain practical insights into techniques for interpreting machine learning models.
Browse courses on
Explainable AI
Show steps
-
Identify online resources or platforms offering tutorials on interpretable machine learning.
-
Choose tutorials that align with your level of expertise and course objectives.
-
Follow the tutorials step-by-step, implementing the techniques in a programming environment.
Show all two activities
Review machine learning basics
Show steps
Revisit the fundamental concepts of machine learning to strengthen your foundation before diving into the course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Re-read introductory chapters from a machine learning textbook or online resource.
- Practice solving basic machine learning problems using a programming language you are familiar with.
- Review common machine learning algorithms and their applications.
Explore tutorials on interpretable machine learning
Show steps
Seek out tutorials to gain practical insights into techniques for interpreting machine learning models.
Browse courses on
Explainable AI
Show steps
Show steps
Show steps
- Identify online resources or platforms offering tutorials on interpretable machine learning.
- Choose tutorials that align with your level of expertise and course objectives.
- Follow the tutorials step-by-step, implementing the techniques in a programming environment.
Show all two activities
Career center
Learners who complete Interpretable Machine Learning Applications: Part 1 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. Those who wish to become Machine Learning Engineers may consider taking this course which covers Interpretable Machine Learning Applications. Knowing how to explain and interpret these models can help Machine Learning Engineers establish trust with users. This can translate to more efficient work and better performance when deployed.
Data Scientist
Data Scientist
Data Scientists use mathematics, statistics, and programming to solve complex problems. They use these skills to extract insights from data and build models that predict future outcomes. This course on Interpretable Machine Learning Applications can help Data Scientists understand how to build models that are easier to explain and interpret.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Those who wish to become Software Engineers may consider taking this course on Interpretable Machine Learning Applications. This is because it can help them build software applications that are more explainable and interpretable.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course on Interpretable Machine Learning Applications can help Product Managers understand how to build products that are more explainable and interpretable. This can help them to make better decisions about which products to develop and how to market them.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. They use data and analysis to make recommendations to businesses. This course on Interpretable Machine Learning Applications can help Business Analysts understand how to use machine learning to solve business problems. It can also help them to communicate the results of their analysis to stakeholders in a clear and concise way.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data. They use this data to identify trends and patterns. This course on Interpretable Machine Learning Applications can help Data Analysts understand how to build machine learning models that are easier to explain and interpret. This can help them to communicate the results of their analysis to stakeholders in a clear and concise way.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use this data to make predictions and draw conclusions. This course on Interpretable Machine Learning Applications can help Statisticians understand how to use machine learning to make more accurate predictions and draw more informed conclusions.
Financial Analyst
Financial Analyst
Financial Analysts collect and analyze financial data. They use this data to make recommendations to investors and businesses. This course on Interpretable Machine Learning Applications can help Financial Analysts understand how to use machine learning to make more accurate predictions and draw more informed conclusions. This can help them to make better recommendations to investors and businesses.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumer behavior. They use this data to help businesses make decisions about their products and marketing strategies. This course on Interpretable Machine Learning Applications can help Market Researchers understand how to use machine learning to collect and analyze data more efficiently.
Consultant
Consultant
Consultants advise businesses on a variety of topics, including strategy, finance, and operations. This course on Interpretable Machine Learning Applications can help Consultants understand how to use machine learning to solve business problems. This can help them to provide better advice to their clients.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. They use data to track the effectiveness of their campaigns and make adjustments as needed. This course on Interpretable Machine Learning Applications can help Marketing Managers understand how to use machine learning to collect and analyze data more efficiently.
Sales Manager
Sales Manager
Sales Managers lead sales teams and develop sales strategies. They use data to track the performance of their teams and make adjustments as needed. This course on Interpretable Machine Learning Applications can help Sales Managers understand how to use machine learning to collect and analyze data more efficiently.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. They use data to track the performance of their operations and make adjustments as needed. This course on Interpretable Machine Learning Applications can help Operations Managers understand how to use machine learning to collect and analyze data more efficiently.
Project Manager
Project Manager
Project Managers plan and execute projects. They use data to track the progress of their projects and make adjustments as needed. This course on Interpretable Machine Learning Applications can help Project Managers understand how to use machine learning to collect and analyze data more efficiently.
Human Resources Manager
Human Resources Manager
Human Resources Managers oversee the human resources department of a business. They use data to track the performance of their employees and make adjustments as needed. This course on Interpretable Machine Learning Applications may be helpful for Human Resources Managers who want to learn how to use machine learning to collect and analyze data more efficiently.
For more career information including salaries, visit:
OpenCourser.com/course/lqnil6/interpretable
Reading list
We've selected 12 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
Interpretable Machine Learning Applications: Part 1.
Provides a comprehensive overview of interpretable machine learning techniques, including decision trees, random forests, and linear models. It covers both the theory and practice of interpretable machine learning, and provides hands-on examples in Python.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and tree-based methods. It covers both the theory and practice of statistical learning, and provides hands-on examples in R.
Save
Provides a comprehensive overview of machine learning algorithms, including both supervised learning and unsupervised learning algorithms. It covers both the theory and practice of machine learning algorithms, and provides hands-on examples in Python.
Provides a comprehensive overview of machine learning techniques for computer vision. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides case studies of how machine learning has been used to solve computer vision problems in a variety of industries.
Provides a comprehensive overview of machine learning techniques for text data. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides case studies of how machine learning has been used to solve text data problems in a variety of industries.
Provides a practical guide to building and deploying machine learning models using the R programming language. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to building and deploying machine learning models using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive overview of machine learning techniques for financial applications. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides case studies of how machine learning has been used to solve financial problems in a variety of industries.
Provides a practical guide to using machine learning to solve business problems. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides case studies of how machine learning has been used to solve business problems in a variety of industries.
Provides a non-technical overview of machine learning, including both the theory and practice of machine learning. It is written in a non-technical style and is accessible to readers with no prior knowledge of machine learning.
Save
Provides a comprehensive overview of deep learning, including both the theory and practice of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a rigorous mathematical treatment of machine learning and pattern recognition. It covers a wide range of topics, including probability theory, Bayesian inference, and kernel methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lqnil6/interpretable
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Effort
2-hour course, including time of video recordings, practicing and readings, taking the quiz.
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Epaminondas Kapetanios
Language
English
Good to know
Suitable for intermediate learners or aspiring professionals in data science and analytics who seek practical applications
Focuses on real-world applications, tailoring to individuals working with business problems that can leverage machine learning insights
Provides hands-on instruction in using industry-relevant tools and techniques for interpretable machine learning
Project-based learning approach allows learners to apply concepts directly to practical scenarios
Guided by instructors with expertise in the practical applications of machine learning in industry
May require further knowledge in programming and statistical concepts for optimal comprehension
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