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Muhammad Saad uddin
In this 2-hour long project-based course, you will learn how to understand the predictions of your model, feature relations, visualize and interpret feature & model relation with statistics and much more.
Register for this course and see more details by visiting:
OpenCourser.com/course/fsye2a/explaining
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What's inside
Syllabus
Explaining machine learning models
you will learn how to understand the predictions of your model, visualize and interpret feature & model relation with statistics and much more.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those interested in understanding model predictions and feature relations for machine learning
Imparts essential skills and knowledge for interpreting and visualizing machine learning models
Incorporates statistical techniques for model interpretation and feature analysis
Save this course
Save Explaining machine learning models to your list so you can find it easily later:
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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 machine learning models with these
activities:
Probability and Statistics for Engineers and Scientists
Show steps
Reading this book will help you gain a deeper understanding of the statistical concepts used in machine learning.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
-
Read the book's chapters on probability and statistics.
-
Work through the practice problems in the book.
-
Apply what you learn to your coursework.
Join a study group
Show steps
Joining a study group will allow you to collaborate with other students and learn from each other.
Show steps
-
Find a study group that meets your needs.
-
Attend study group meetings regularly.
-
Participate in discussions and ask questions.
-
Help other students with their understanding.
Create a set of flashcards
Show steps
Creating flashcards will help you understand and memorize the key concepts of the course.
Browse courses on
Machine Learning
Show steps
-
Identify the key concepts from the course lectures and readings.
-
Write down a question on one side of the flashcard and the answer on the other side.
-
Review your flashcards regularly to test your understanding.
-
Create an online quiz with your flashcards.
-
Use an app to create and study flashcards.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems
Show steps
Solving practice problems will help you apply the concepts you learn in the course to real-world situations.
Browse courses on
Model Evaluation
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems on your own.
-
Check your answers against the provided solutions.
-
Identify your areas of weakness and focus on improving them.
Watch online tutorials on related topics.
Show steps
Watching online tutorials will allow you to learn more about the concepts you learn in the course.
Show steps
-
Find online tutorials on related topics.
-
Watch the tutorials and take notes.
-
Apply what you learn to your coursework.
Attend a workshop on machine learning
Show steps
Attending a workshop will allow you to learn more about machine learning from experts in the field.
Browse courses on
Machine Learning
Show steps
-
Find a workshop on machine learning that interests you.
-
Attend the workshop and take notes.
-
Network with other attendees and learn from their experiences.
-
Apply what you learn to your coursework.
Develop a machine learning model
Show steps
Developing a machine learning model will allow you to apply the concepts you learn in the course to a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning project that interests you.
-
Gather data for your project.
-
Develop a machine learning model for your project.
-
Train and evaluate your model.
-
Deploy your model and make predictions.
Probability and Statistics for Engineers and Scientists
Show steps
Reading this book will help you gain a deeper understanding of the statistical concepts used in machine learning.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on probability and statistics.
- Work through the practice problems in the book.
- Apply what you learn to your coursework.
Join a study group
Show steps
Joining a study group will allow you to collaborate with other students and learn from each other.
Show steps
Show steps
Show steps
- Find a study group that meets your needs.
- Attend study group meetings regularly.
- Participate in discussions and ask questions.
- Help other students with their understanding.
Create a set of flashcards
Show steps
Creating flashcards will help you understand and memorize the key concepts of the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify the key concepts from the course lectures and readings.
- Write down a question on one side of the flashcard and the answer on the other side.
- Review your flashcards regularly to test your understanding.
- Create an online quiz with your flashcards.
- Use an app to create and study flashcards.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems
Show steps
Solving practice problems will help you apply the concepts you learn in the course to real-world situations.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems on your own.
- Check your answers against the provided solutions.
- Identify your areas of weakness and focus on improving them.
Watch online tutorials on related topics.
Show steps
Watching online tutorials will allow you to learn more about the concepts you learn in the course.
Show steps
Show steps
Show steps
- Find online tutorials on related topics.
- Watch the tutorials and take notes.
- Apply what you learn to your coursework.
Attend a workshop on machine learning
Show steps
Attending a workshop will allow you to learn more about machine learning from experts in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a workshop on machine learning that interests you.
- Attend the workshop and take notes.
- Network with other attendees and learn from their experiences.
- Apply what you learn to your coursework.
Develop a machine learning model
Show steps
Developing a machine learning model will allow you to apply the concepts you learn in the course to a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning project that interests you.
- Gather data for your project.
- Develop a machine learning model for your project.
- Train and evaluate your model.
- Deploy your model and make predictions.
Career center
Learners who complete Explaining machine learning models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists design, construct, and deploy many different kinds of machine learning models. Machine learning models are useful in many different computer science fields such as data science, statistics, computer science, and IT. This course will help you learn the theory behind building machine learning models but it will also teach you the practical skills needed to build, deply, and maintain models in the real-world. Understanding the predictions of machine learning models is a critical skill that Data Scientists use every day.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build pipelines to deploy machine learning models into production environments. This course will help you with the essential skills needed to understand the predictions and outputs of different kinds of machine learning models. Understanding machine learning models is essential to the role of a Machine Learning Engineer since you will work with many different models on a daily basis.
Data Analyst
Data Analyst
Data Analysts design, deploy, and maintain data products that provide business value. Machine learning models are a type of data product. Data Analysts often build, test, deploy, and maintain machine learning models. This course will help you understand the theory and practice behind building and deploying machine learning models. It will also help you to interpret and visualize the predictions and outputs of different types of machine learning models. This will help you to build and maintain data products that provide business value.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. Many modern software systems integrate with machine learning models. This course will help you to understand the theory and practice of building, deploying, and maintaining machine learning models. This will enable you to design, build, and maintain software systems that integrate with machine learning models.
Business Analyst
Business Analyst
Business Analysts define and manage requirements for business projects. Machine learning models are often used to determine requirements for business projects. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you to define and manage requirements for business projects.
Project Manager
Project Manager
Project Managers plan and execute many kinds of projects, including those that build and deploy machine learning models. This course may help you understand the theory and practice behind building and deploying machine learning models. This may help you to plan and execute projects that build and deploy machine learning models.
Product Manager
Product Manager
Product Managers define and manage requirements for products and features. Machine learning models are a type of feature. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you to define and manage requirements for products that include features that integrate with or are based on machine learning.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. Machine learning models are often used in data pipelines. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you design, build, and maintain data pipelines that integrate with or include machine learning models.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. Machine learning models are a kind of statistical model. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you to collect, analyze, interpret, and present data using machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use advanced analytical techniques to help organizations make better decisions. Machine learning is the field of study that develops advanced analytical techniques. This course may help you to understand the theory and practice behind using machine learning techniques to make better decisions.
Financial Analyst
Financial Analyst
Financial Analysts collect, analyze, and interpret financial data to help organizations make better decisions. Machine learning models are useful for analyzing financial data. This course may help you to understand the theory and practice behind building and deploying machine learning models for financial data. This may help you to collect, analyze, and interpret financial data using machine learning models.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret market data to help organizations make better decisions. Machine learning models are useful for analyzing market data. This course may help you to understand the theory and practice behind building and deploying machine learning models for market data. This may help you to collect, analyze, and interpret market data using machine learning models.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. Machine learning models are a kind of mathematical and statistical technique. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you to assess risk and uncertainty using machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze and manage financial risks. Machine learning models are a kind of mathematical and statistical technique. This course may help you to understand the theory and practice behind building and deploying machine learning models. This may help you to analyze and manage financial risks using machine learning.
Auditor
Auditor
Auditors examine and evaluate financial records and other information to ensure accuracy and compliance. Machine learning models are useful for examining and evaluating financial records and other information. This course may help you to understand the theory and practice behind building and deploying machine learning models for examining and evaluating financial records and other information. This may help you to ensure accuracy and compliance using machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/fsye2a/explaining
Reading list
We've selected 11 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 machine learning models.
Practical guide to building and evaluating interpretable machine learning models. It covers a wide range of techniques, including local and global interpretability methods, and provides hands-on examples and case studies. It complements the course by offering a practical perspective on the application of explainability methods.
Provides a practical introduction to data science concepts and techniques, with a focus on business applications. It covers the entire data science pipeline, from data collection and cleaning to model building and deployment. It complements the course by providing a broader perspective on the role of explainability in data science projects.
Classic textbook on statistical learning methods, providing a comprehensive overview of both supervised and unsupervised learning techniques. It serves as a valuable reference for the statistical foundations of explainability methods, particularly for those interested in the theoretical aspects of the subject.
Save
Provides a practical guide to visualizing data effectively, covering a wide range of techniques and tools. It complements the course by emphasizing the importance of visualization in communicating and understanding machine learning models and their predictions.
Provides a comprehensive guide to machine learning using the R programming language. It covers a wide range of machine learning algorithms and techniques, with a focus on practical implementation. It complements the course by providing hands-on experience with building and evaluating machine learning models in R.
Provides a comprehensive guide to data analysis using the Python programming language. It covers a wide range of data analysis techniques, from data cleaning and manipulation to data visualization and modeling. It complements the course by providing hands-on experience with building and evaluating machine learning models in Python.
Provides a comprehensive guide to deep learning using the Python programming language. It covers a wide range of deep learning architectures and techniques, with a focus on practical implementation. It complements the course by providing hands-on experience with building and evaluating deep learning models in Python.
Provides a comprehensive guide to natural language processing using the Python programming language. It covers a wide range of natural language processing techniques, from text classification and sentiment analysis to machine translation and chatbots. It complements the course by providing hands-on experience with building and evaluating natural language processing models in Python.
Save
Provides a practical guide to machine learning using the WEKA software suite. It covers a wide range of machine learning algorithms and techniques, with a focus on practical implementation. It complements the course by providing hands-on experience with building and evaluating machine learning models in WEKA.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of machine learning algorithms and techniques, with a focus on the underlying mathematical foundations. It complements the course by providing a deeper understanding of the theoretical underpinnings of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of machine learning algorithms and techniques, with a focus on the underlying mathematical foundations. It complements the course by providing a deeper understanding of the theoretical underpinnings of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fsye2a/explaining
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Language
English
Good to know
Suitable for those interested in understanding model predictions and feature relations for machine learning
Imparts essential skills and knowledge for interpreting and visualizing machine learning models
Incorporates statistical techniques for model interpretation and feature analysis
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