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Epaminondas Kapetanios
In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered.
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches how to analyze the behavior of prediction models, which is useful for ML developers and data scientists
Uses the 'What-If' Tool, which is a well-known visualization widget for ML models
Designed for learners with a basic understanding of Python and machine learning
Focuses on practical application rather than theoretical concepts
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Reviews summary
Practical what-if tool for interpretable ml
According to learners, this course is a highly focused guided project providing a practical introduction to the What-If Tool (WIT) for interpretable machine learning. Students appreciate the hands-on experience in a Google Colab environment, making it accessible for a quick dive into analyzing model behavior. Many found the instructions clear and easy to follow, enabling them to grasp basic concepts of WIT efficiently. While it serves as an excellent starting point for understanding how to interpret ML predictions at both individual and global levels, some note its brief duration means it covers only foundational aspects, implying a need for further exploration for advanced applications.
Emphasizes hands-on learning with practical examples in a coding environment.
"The hands-on activities in Google Colab were incredibly helpful for understanding WIT."
"I appreciated the practical approach; it wasn't just theory, but real application."
"Working directly with the code made the concepts of model interpretation much clearer."
A concise introduction to the What-If Tool for ML model interpretation.
"This project gave me a great first look at using the What-If Tool effectively."
"I found the demonstration of WIT's capabilities for interpreting models very insightful."
"It's an excellent way to quickly learn how to use the What-If Tool in a practical setting."
Beneficial for learners with prior understanding of machine learning basics.
"I recommend having a basic grasp of ML concepts before starting, it helps a lot."
"This project assumes you're already familiar with models like Decision Trees and Random Forests."
"If you're new to ML, this might move a bit fast on core concepts, but the WIT part is still clear."
A short project, offering foundational knowledge rather than in-depth coverage.
"As a 1-hour project, it's a quick overview, so don't expect deep dives into every aspect."
"I found it a good starting point, but I'll need to seek additional resources for more advanced topics."
"It's effective for what it is—a brief introduction—but naturally, it can't cover everything."
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 4 with these
activities:
Organize Course Materials
Show steps
Organizing the course materials is key to effective learning and efficient revision.
Show steps
-
Review course materials and identify key concepts and topics.
-
Create a structured system for organizing notes, assignments, quizzes, and exams, both physically and digitally.
-
Regularly review and update your organized materials to reinforce your understanding and improve retention.
Machine Learning Algorithms Tutorial
Show steps
This tutorial will provide a strong foundation in ML algorithms, which is essential for understanding how WIT analyzes the behavior of ML prediction models.
Browse courses on
Machine Learning Algorithms
Show steps
-
Review the fundamental concepts of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
-
Learn about different types of ML algorithms and their applications.
-
Explore the strengths and limitations of different ML algorithms to make informed choices in future projects.
Attend an ML Meetup
Show steps
Connect with other ML practitioners, share knowledge, and learn about the latest trends and tools in the field.
Show steps
-
Research and find an ML meetup in your area or online.
-
Attend the meetup and actively participate in discussions. Ask questions, share your own insights, listen to others, and be open to new ideas.
-
Network with other attendees. Exchange contact information, follow them on social media, and stay connected.
Four other activities
Expand to see all activities and additional details
Show all seven activities
What-if Tool Tutorial
Show steps
The objective of this guided tutorial is to provide a step-by-step guide on using WIT to analyze the behavior of ML prediction models.
Show steps
-
Follow the tutorial steps to set up a machine learning application in Python.
-
Learn how to import and prepare the data for ML modeling.
-
Train and test classifiers as prediction models in Python.
-
Analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis).
-
Analyze the behavior of the trained prediction models by using WIT on a global basis (all test data considered).
Analyze WIT for Prediction Models
Show steps
These practice drills will help solidify your understanding of how to use WIT to analyze the behavior of ML prediction models.
Show steps
-
Complete a series of exercises that involve using WIT to analyze the behavior of different types of ML prediction models.
-
Identify the key factors that influence the predictions of ML models using WIT.
-
Apply WIT to real-world datasets to gain insights into the behavior of ML models in practice.
Personal Project with WIT
Show steps
Engaging in a personal project will allow you to apply your knowledge of WIT in a practical setting and showcase your skills.
Show steps
-
Identify a real-world problem that you can solve using WIT.
-
Design and implement a solution using WIT to analyze the behavior of an ML prediction model.
-
Write a report and/or create a presentation to document your project and share your findings.
WIT Analysis Report
Show steps
This activity will provide practical experience to communicate technical findings effectively and hone your data analysis and visualization skills.
Show steps
-
Select a real-world dataset and apply WIT to analyze the behavior of an ML prediction model trained on the dataset.
-
Write a report that summarizes your findings, including insights into the key factors that influence the model's predictions.
-
Create visualizations to illustrate your findings and make them more accessible to a non-technical audience.
-
Present your report to peers or a broader audience to share your knowledge and insights.
Organize Course Materials
Show steps
Organizing the course materials is key to effective learning and efficient revision.
Show steps
Show steps
Show steps
- Review course materials and identify key concepts and topics.
- Create a structured system for organizing notes, assignments, quizzes, and exams, both physically and digitally.
- Regularly review and update your organized materials to reinforce your understanding and improve retention.
Machine Learning Algorithms Tutorial
Show steps
This tutorial will provide a strong foundation in ML algorithms, which is essential for understanding how WIT analyzes the behavior of ML prediction models.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Review the fundamental concepts of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
- Learn about different types of ML algorithms and their applications.
- Explore the strengths and limitations of different ML algorithms to make informed choices in future projects.
Attend an ML Meetup
Show steps
Connect with other ML practitioners, share knowledge, and learn about the latest trends and tools in the field.
Show steps
Show steps
Show steps
- Research and find an ML meetup in your area or online.
- Attend the meetup and actively participate in discussions. Ask questions, share your own insights, listen to others, and be open to new ideas.
- Network with other attendees. Exchange contact information, follow them on social media, and stay connected.
Four other activities
Expand to see all activities and additional details
Show all seven activities
What-if Tool Tutorial
Show steps
The objective of this guided tutorial is to provide a step-by-step guide on using WIT to analyze the behavior of ML prediction models.
Show steps
Show steps
Show steps
- Follow the tutorial steps to set up a machine learning application in Python.
- Learn how to import and prepare the data for ML modeling.
- Train and test classifiers as prediction models in Python.
- Analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis).
- Analyze the behavior of the trained prediction models by using WIT on a global basis (all test data considered).
Analyze WIT for Prediction Models
Show steps
These practice drills will help solidify your understanding of how to use WIT to analyze the behavior of ML prediction models.
Show steps
Show steps
Show steps
- Complete a series of exercises that involve using WIT to analyze the behavior of different types of ML prediction models.
- Identify the key factors that influence the predictions of ML models using WIT.
- Apply WIT to real-world datasets to gain insights into the behavior of ML models in practice.
Personal Project with WIT
Show steps
Engaging in a personal project will allow you to apply your knowledge of WIT in a practical setting and showcase your skills.
Show steps
Show steps
Show steps
- Identify a real-world problem that you can solve using WIT.
- Design and implement a solution using WIT to analyze the behavior of an ML prediction model.
- Write a report and/or create a presentation to document your project and share your findings.
WIT Analysis Report
Show steps
This activity will provide practical experience to communicate technical findings effectively and hone your data analysis and visualization skills.
Show steps
Show steps
Show steps
- Select a real-world dataset and apply WIT to analyze the behavior of an ML prediction model trained on the dataset.
- Write a report that summarizes your findings, including insights into the key factors that influence the model's predictions.
- Create visualizations to illustrate your findings and make them more accessible to a non-technical audience.
- Present your report to peers or a broader audience to share your knowledge and insights.
Career center
Learners who complete Interpretable Machine Learning Applications: Part 4 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use a wide range of tools to analyze data, from statistics to machine learning to visualization. They work with data from a variety of sources, including structured and unstructured data, to find patterns and insights that can help organizations make better decisions. This course can help you develop the skills you need to be a successful Data Scientist, including how to use machine learning to build predictive models, how to interpret and visualize data, and how to communicate your findings to stakeholders.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work closely with Data Scientists to develop the algorithms and models that power machine learning applications. This course can help you develop the skills you need to be a successful Machine Learning Engineer, including how to build and train machine learning models, how to evaluate and optimize model performance, and how to deploy models to production.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make decisions. They work with data from a variety of sources, including structured and unstructured data, to identify trends and patterns. This course can help you develop the skills you need to be a successful Data Analyst, including how to collect and clean data, how to analyze data using statistical and machine learning techniques, and how to communicate your findings to stakeholders.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and develop solutions. They work with stakeholders across the organization to gather requirements, analyze data, and develop recommendations. This course can help you develop the skills you need to be a successful Business Analyst, including how to collect and analyze data, how to use data to develop recommendations, and how to communicate your findings to stakeholders.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to define the product vision, set the product roadmap, and track product progress. This course can help you develop the skills you need to be a successful Product Manager, including how to use data to understand customer needs, how to develop and launch new products, and how to track product performance.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They work with marketers to develop and execute marketing campaigns, and track campaign performance. This course can help you develop the skills you need to be a successful Marketing Analyst, including how to collect and analyze data, how to use data to develop marketing campaigns, and how to track campaign performance.
Sales Analyst
Sales Analyst
Sales Analysts use data to understand sales trends and develop sales strategies. They work with sales teams to identify opportunities, develop sales strategies, and track sales performance. This course can help you develop the skills you need to be a successful Sales Analyst, including how to collect and analyze data, how to use data to develop sales strategies, and how to track sales performance.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment recommendations. They work with investors to develop investment strategies and track investment performance. This course can help you develop the skills you need to be a successful Financial Analyst, including how to collect and analyze data, how to use data to develop investment strategies, and how to track investment performance.
Operations Analyst
Operations Analyst
Operations Analysts use data to improve operational efficiency. They work with operations teams to identify bottlenecks, develop solutions, and track operational performance. This course can help you develop the skills you need to be a successful Operations Analyst, including how to collect and analyze data, how to use data to develop solutions, and how to track operational performance.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and manage risks. They work with businesses to develop risk management strategies and track risk exposure. This course can help you develop the skills you need to be a successful Risk Analyst, including how to collect and analyze data, how to use data to develop risk management strategies, and how to track risk exposure.
Statistician
Statistician
Statisticians use data to understand patterns and trends. They work with researchers and scientists to design studies, collect data, and analyze data. This course can help you develop the skills you need to be a successful Statistician, including how to collect and analyze data, how to use data to develop models, and how to communicate your findings to stakeholders.
Economist
Economist
Economists use data to understand economic trends and develop economic policies. They work with governments and businesses to develop economic policies and track economic performance. This course can help you develop the skills you need to be a successful Economist, including how to collect and analyze data, how to use data to develop economic policies, and how to track economic performance.
Epidemiologist
Epidemiologist
Epidemiologists use data to understand and control the spread of disease. They work with public health officials to develop public health policies and track disease outbreaks. This course can help you develop the skills you need to be a successful Epidemiologist, including how to collect and analyze data, how to use data to develop public health policies, and how to track disease outbreaks.
Actuary
Actuary
Actuaries use data to assess and manage risk. They work with insurance companies and pension funds to develop insurance policies and track risk exposure. This course can help you develop the skills you need to be a successful Actuary, including how to collect and analyze data, how to use data to develop insurance policies, and how to track risk exposure.
Data Architect
Data Architect
Data Architects design and build data infrastructure. They work with organizations to develop data management strategies and design data systems. This course may be helpful for Data Architects who want to learn more about how to use machine learning to build predictive models and how to interpret and visualize data.
For more career information including salaries, visit:
OpenCourser.com/course/iqb4ph/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 4.
Provides a comprehensive overview of interpretable machine learning techniques, including both model-agnostic and model-specific methods. It valuable resource for anyone who wants to understand how to make machine learning models more interpretable and trustworthy.
Provides a comprehensive overview of machine learning from a probabilistic perspective, including the different types of probabilistic machine learning algorithms and their applications. It valuable resource for anyone who wants to learn more about probabilistic machine learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning, including the different types of Bayesian machine learning algorithms and their applications. It valuable resource for anyone who wants to learn more about Bayesian machine learning.
Provides a comprehensive overview of machine learning, including the different types of machine learning algorithms and their applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of machine learning, including the different types of machine learning algorithms and their applications. It valuable resource for anyone who wants to learn more about machine learning.
Save
Provides a comprehensive overview of deep learning, including the different types of deep learning models and their applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of statistical learning, including the different types of statistical learning algorithms and their applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of statistical learning, including the different types of statistical learning algorithms and their applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a practical introduction to machine learning with Python, including how to use scikit-learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn how to build and train machine learning models.
Provides a practical introduction to machine learning with Python, including how to use scikit-learn to build and train machine learning models. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
Provides a practical introduction to machine learning for non-programmers, including how to use machine learning libraries to build and train machine learning models. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
Provides a non-technical overview of machine learning, including the different types of machine learning algorithms and their applications. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iqb4ph/interpretable
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