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Muhammad Saad uddin
In this 2 hour guided project you will learn how to deal with imbalance classification problems in a profound manner, applying several resampling strategies and visualizing the effects of resampling on imbalance classification dataset.
Note: This project...
Read more
In this 2 hour guided project you will learn how to deal with imbalance classification problems in a profound manner, applying several resampling strategies and visualizing the effects of resampling on imbalance classification dataset.
Note: This project...
Read more
In this 2 hour guided project you will learn how to deal with imbalance classification problems in a profound manner, applying several resampling strategies and visualizing the effects of resampling on imbalance classification dataset.
Note: This project 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/mak8yo/effectively
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possible dealbreakers
Well-designed course for learners interested in data science or machine learning
Suitable for learners with prior knowledge of classification algorithms and Python
Provides a thorough understanding of resampling strategies for imbalanced datasets
Guided project format allows learners to apply concepts hands-on
May not align well with learners from regions outside North America
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Save Effectively Dealing with Imbalance Classes to your list so you can find it easily later:
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Reviews summary
Imbalanced data handling techniques
Students enjoyed this course that covered a variety of techniques for handling imbalanced data sets. They noted that the course provided many helpful metrics and visualizations. Students did encounter some technical issues with the sound quality and course platform, but overall felt that the course was positive and emphasized the importance of visualizations and metrics.
Covers various techniques for handling imbalanced data sets.
"Covers various techniques for handling imbalanced data sets."
Emphasis on visualizations and metrics.
"Mainly because of the emphasis on visualizations and metrics."
Audio is sometimes hard to hear.
"Audio is sometimes hard to hear."
Rhyme platform is problematic.
"The Rhyme platform is problematic."
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 Effectively Dealing with Imbalance Classes with these
activities:
Organize course materials for better retention
Show steps
Organizing your course materials will help you retain the information better.
Show steps
-
Review the course syllabus and identify key topics.
-
Create a system for organizing your notes, assignments, and other materials.
Refresher on data sets and data manipulation
Show steps
Refreshing your data sets and data manipulation skills will give a boost to your learning in this course
Show steps
-
Review the basics of data sets and data manipulation in Python.
-
Complete a few practice exercises on data sets.
Read a book on data analysis for deeper understanding
Show steps
Reading a book on data analysis will provide a comprehensive understanding of the subject and supplement the course material.
Show steps
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Review fundamental data analysis concepts with peers
Show steps
Discussing fundamental data analysis concepts with peers will reinforce your understanding and provide new perspectives.
Show steps
-
Form a study group with other students in this course.
-
Schedule regular meetings to review course material and discuss concepts.
Follow online tutorials on imbalanced classification
Show steps
Reinforce your understanding of resampling methods and their practical applications.
Show steps
-
Identify reliable online tutorials covering imbalanced classification techniques.
-
Follow the tutorials step-by-step, implementing the methods on your own datasets.
-
Experiment with different parameters and settings to observe their impact on results.
Practice data resampling techniques
Show steps
Completing practice drills on data resampling will reinforce your understanding of the concepts presented in the course.
Show steps
-
Visit online platforms like LeetCode or HackerRank for practice problems on data resampling.
-
Work through a series of guided tutorials on data resampling.
Solve imbalance classification problems on datasets
Show steps
Practice applying resampling techniques to address challenges in imbalance classification.
Show steps
-
Choose a suitable dataset with class imbalance.
-
Apply various resampling strategies (e.g., oversampling, undersampling).
-
Evaluate the performance of models trained on resampled datasets.
-
Compare the effects of different resampling techniques on classification accuracy.
Visualize data resampling effects
Show steps
Creating visualizations of data resampling effects will solidify your understanding of how different resampling techniques impact data distribution.
Show steps
-
Choose a data set and apply different resampling techniques to it.
-
Use data visualization tools to create visual representations of the resampled data.
Create a presentation on imbalance classification techniques
Show steps
Demonstrate your comprehension of imbalance classification by presenting your findings to an audience.
Show steps
-
Gather information and data on imbalance classification.
-
Design and create a visually appealing presentation.
-
Practice delivering the presentation clearly and confidently.
-
Present your findings to an audience and engage in discussions.
Start a data analysis project to apply your skills
Show steps
Starting a data analysis project will provide practical experience and allow you to apply the skills learned in the course.
Show steps
-
Identify a real-world problem that can be solved using data analysis.
-
Collect and clean the necessary data.
-
Apply data analysis techniques to explore and analyze the data.
Organize course materials for better retention
Show steps
Organizing your course materials will help you retain the information better.
Show steps
Show steps
Show steps
- Review the course syllabus and identify key topics.
- Create a system for organizing your notes, assignments, and other materials.
Refresher on data sets and data manipulation
Show steps
Refreshing your data sets and data manipulation skills will give a boost to your learning in this course
Show steps
Show steps
Show steps
- Review the basics of data sets and data manipulation in Python.
- Complete a few practice exercises on data sets.
Read a book on data analysis for deeper understanding
Show steps
Reading a book on data analysis will provide a comprehensive understanding of the subject and supplement the course material.
Show steps
Show steps
Show steps
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Review fundamental data analysis concepts with peers
Show steps
Discussing fundamental data analysis concepts with peers will reinforce your understanding and provide new perspectives.
Show steps
Show steps
Show steps
- Form a study group with other students in this course.
- Schedule regular meetings to review course material and discuss concepts.
Follow online tutorials on imbalanced classification
Show steps
Reinforce your understanding of resampling methods and their practical applications.
Show steps
Show steps
Show steps
- Identify reliable online tutorials covering imbalanced classification techniques.
- Follow the tutorials step-by-step, implementing the methods on your own datasets.
- Experiment with different parameters and settings to observe their impact on results.
Practice data resampling techniques
Show steps
Completing practice drills on data resampling will reinforce your understanding of the concepts presented in the course.
Show steps
Show steps
Show steps
- Visit online platforms like LeetCode or HackerRank for practice problems on data resampling.
- Work through a series of guided tutorials on data resampling.
Solve imbalance classification problems on datasets
Show steps
Practice applying resampling techniques to address challenges in imbalance classification.
Show steps
Show steps
Show steps
- Choose a suitable dataset with class imbalance.
- Apply various resampling strategies (e.g., oversampling, undersampling).
- Evaluate the performance of models trained on resampled datasets.
- Compare the effects of different resampling techniques on classification accuracy.
Visualize data resampling effects
Show steps
Creating visualizations of data resampling effects will solidify your understanding of how different resampling techniques impact data distribution.
Show steps
Show steps
Show steps
- Choose a data set and apply different resampling techniques to it.
- Use data visualization tools to create visual representations of the resampled data.
Create a presentation on imbalance classification techniques
Show steps
Demonstrate your comprehension of imbalance classification by presenting your findings to an audience.
Show steps
Show steps
Show steps
- Gather information and data on imbalance classification.
- Design and create a visually appealing presentation.
- Practice delivering the presentation clearly and confidently.
- Present your findings to an audience and engage in discussions.
Start a data analysis project to apply your skills
Show steps
Starting a data analysis project will provide practical experience and allow you to apply the skills learned in the course.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using data analysis.
- Collect and clean the necessary data.
- Apply data analysis techniques to explore and analyze the data.
Career center
Learners who complete Effectively Dealing with Imbalance Classes will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
**Data Analysts** work as part of a team or independently to provide statistical analysis for complex data. They apply advanced statistical techniques to solve problems, reveal trends, and make predictions. This course may be helpful in providing a foundation in dealing with imbalanced classification problems, which is a common challenge in this field.
Data Scientist
Data Scientist
**Data Scientists** use advanced statistical methods and machine learning algorithms to extract insights from data. They work with large and complex datasets to identify patterns and trends that can be used to make better decisions. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in data analysis and machine learning techniques.
Machine Learning Engineer
Machine Learning Engineer
**Machine Learning Engineers** design, develop, and maintain machine learning models. They work with data scientists to translate business problems into technical requirements and develop models that can solve these problems. This course may be helpful in providing a foundation in dealing with imbalanced classification problems, which is a common challenge in this field.
Data Engineer
Data Engineer
**Data Engineers** design, build, and maintain the infrastructure needed to store, process, and analyze data. They work with data analysts and data scientists to ensure that data is accessible and reliable. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in data management and processing techniques.
Business Analyst
Business Analyst
**Business Analysts** use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop recommendations. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in data analysis and visualization techniques.
Quantitative Analyst
Quantitative Analyst
**Quantitative Analysts** use mathematical and statistical models to analyze financial data. They work with investment managers to develop trading strategies and manage risk. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in statistical modeling and analysis techniques.
Statistician
Statistician
**Statisticians** design, conduct, and interpret statistical studies. They work with data to collect, analyze, and interpret data. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in statistical methods and analysis techniques.
Operations Research Analyst
Operations Research Analyst
**Operations Research Analysts** use mathematical and analytical techniques to solve complex problems. They work with businesses to improve efficiency and productivity. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in optimization and decision-making techniques.
Market Research Analyst
Market Research Analyst
**Market Research Analysts** collect and analyze data to understand consumer behavior. They work with businesses to develop marketing strategies and products. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in data analysis and visualization techniques.
Financial Analyst
Financial Analyst
**Financial Analysts** evaluate and make recommendations on investments. They work with clients to develop financial plans and manage risk. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in financial analysis and modeling techniques.
Risk Analyst
Risk Analyst
**Risk Analysts** identify, assess, and manage risks. They work with businesses to develop risk management strategies and plans. While not directly focused on imbalanced classification, this course may be helpful in providing a foundation in risk assessment and management techniques.
Software Engineer
Software Engineer
**Software Engineers** design, develop, and maintain software systems. This course may be helpful in providing a foundation in software development and coding techniques.
Computer Scientist
Computer Scientist
**Computer Scientists** research and develop new computer technologies. This course may be helpful in providing a foundation in computer science and research techniques.
Data Visualization Specialist
Data Visualization Specialist
**Data Visualization Specialists** design and create visualizations that communicate data effectively. This course may be helpful in providing a foundation in data visualization techniques and tools.
Database Administrator
Database Administrator
**Database Administrators** design, implement, and maintain databases. This course may be helpful in providing a foundation in database management and administration techniques.
For more career information including salaries, visit:
OpenCourser.com/course/mak8yo/effectively
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
Effectively Dealing with Imbalance Classes.
Provides a comprehensive overview of reinforcement learning concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of reinforcement learning.
Provides a comprehensive overview of deep learning concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of deep learning.
Provides a comprehensive overview of speech and language processing concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of speech and language processing.
Save
Provides a comprehensive overview of generative adversarial networks (GANs) concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of GANs.
Save
Provides a comprehensive overview of data mining concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of data mining.
Provides a comprehensive overview of natural language processing concepts and techniques, including a discussion of imbalance classes. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of natural language processing.
Provides practical guidance on how to implement machine learning algorithms using popular Python libraries. It includes a chapter on dealing with imbalance classes, which provides step-by-step instructions on how to apply resampling techniques.
Provides a practical guide to using data science to solve business problems. It includes a chapter on dealing with imbalance classes, which provides real-world examples of how to apply resampling techniques to improve the performance of machine learning models.
Provides a comprehensive overview of machine learning concepts and techniques using the R programming language. It includes a chapter on dealing with imbalance classes, which provides a clear and concise explanation of the different resampling techniques that can be used.
Save
Provides a comprehensive overview of data mining concepts and techniques using the R programming language. It includes a chapter on dealing with imbalance classes, which provides a clear and concise explanation of the different resampling techniques that can be used.
Provides a gentle introduction to machine learning concepts and techniques. It includes a chapter on dealing with imbalance classes, which provides a clear and concise explanation of the different resampling techniques that can be used.
Provides a gentle introduction to data science concepts and techniques. It includes a chapter on dealing with imbalance classes, which provides a clear and concise explanation of the different resampling techniques that can be used.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mak8yo/effectively
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Good to know
Well-designed course for learners interested in data science or machine learning
Suitable for learners with prior knowledge of classification algorithms and Python
Provides a thorough understanding of resampling strategies for imbalanced datasets
Guided project format allows learners to apply concepts hands-on
May not align well with learners from regions outside North America
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