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Bhaskarjit Sarmah
In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and adjust probability threshold to improve selected evaluation metric of the model.
Read more
In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and adjust probability threshold to improve selected evaluation metric of the model.
Read more
In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and adjust probability threshold to improve selected evaluation metric of the model.
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/tbve55/handling
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What's inside
Syllabus
Project Overview
Welcome to this project-based course on Handling Imbalanced Data Classification Problems. In this project, you will learn how to apply various data resampling techniques like undersampling, oversampling, SMOTE on the imbalanced datasets and be able to build a classifier to identify or predict the minority class samples. By the end of this 2-hour long project, you will be able to understand what imbalanced datasets are, what are the evaluation metrics that we should consider while building imbalanced data classification models. You will build predictive models after resampling to balance the classes of target variables and use ROC curve to adjust probability threshold which will help you to improve the evaluation metric of your choice.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers hands-on labs and interactive materials, which can help learners put their newfound knowledge to the test and gain a better understanding of how to handle imbalanced data classification problems
Taught by recognized experts in the field, Bhaskarjit Sarmah, ensuring learners are being taught by industry-leading professionals
Builds a strong foundation for beginners in the field of imbalanced data classification problems, providing a solid base of knowledge and skills to build upon
Develops professional skills and deep expertise in handling imbalanced data classification problems, making it ideal for those looking to advance their careers or skillsets
Provides a comprehensive study of strategies for handling imbalanced data classification problems, ensuring learners gain a well-rounded understanding of the topic
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Reviews summary
Engaging course in imbalanced classification problems
Learners say this course offers engaging assignments with practical content. They appreciate the use of real-world examples and clear explanations. However, some students found the course too easy and would have preferred more challenging content, such as missing data in the data set, parameter tuning, and outlier data.
Provides clear explanations and guidance
"Amazing course!! Thanks to the teacher for making contents easy to understand and incur the knowledge...."
"I especially liked how the instructor made us understand what we were doing before we started and how after every task, he didn't forget to assign some extra exploratory work you could do in that task."
Relevant, real world examples and solutions.
"Practical content, very well explained."
"Introduced to me the concept of SMOTE and how to use it for imbalanced datasets."
"I especially liked how the instructor made us understand what we were doing before we started and how after every task, he didn't forget to assign some extra exploratory work you could do in that task."
Guided project has unrecoverable bug.
"Guided project had an unrecoverable bug, and I could not complete it."
Course is too easy and lacks more advanced topics or challenges.
"It is too easy. There is no missing data in the dataset, parameter tuning, outlier data, etc."
"It is a good class for an intermediate level"
"The course is good."
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 Handling Imbalanced Data Classification Problems with these
activities:
Review the book "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking"
Show steps
Provide a strong foundation in data science, data mining, and data analytic thinking, which are all key topics covered in this course.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the book's introduction and first three chapters.
-
Complete the exercises at the end of each chapter.
Follow online tutorials on imbalanced data classification
Show steps
Supplement your learning by following online tutorials that provide step-by-step guidance on the different aspects of imbalanced data classification.
Show steps
-
Search for tutorials on imbalanced data classification
-
Select tutorials that cover the specific topics you need to learn
-
Follow the tutorials and complete the exercises
Join a study group or participate in online discussions
Show steps
Engaging in discussions and collaborating with peers will help you solidify your understanding, gain different perspectives, and identify areas where you need further clarification.
Show steps
-
Find a study group or online discussion forum
-
Participate actively in discussions
-
Share your knowledge and insights with others
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Join a study group to discuss imbalanced data classification problems.
Show steps
Facilitate collaboration and knowledge sharing, helping you to better understand different perspectives on imbalanced data classification.
Show steps
-
Find a study group.
-
Participate in discussions.
Practice identifying imbalanced data classification problems.
Show steps
Prepare you to effectively handle imbalanced data, which is a common challenge in real-world datasets.
Show steps
-
Find a dataset with imbalanced classes.
-
Plot a histogram of the class distribution.
Complete online tutorials on imbalanced data classification techniques
Show steps
Access external references to further refine your knowledge of imbalanced data classification techniques.
Show steps
-
Identify reputable online tutorials on imbalanced data classification techniques
-
Complete the tutorials, taking notes and practicing the techniques
-
Review the tutorials and practice the techniques regularly
Follow a tutorial on how to use SMOTE to oversample minority class data.
Show steps
Provide practical guidance on using SMOTE, a technique for oversampling minority class data to improve model performance.
Browse courses on
SMOTE
Show steps
-
Find a tutorial on SMOTE.
-
Follow the tutorial to implement SMOTE.
Solve practice problems
Show steps
Practice solving a variety of problems related to imbalanced data classification to improve your understanding of the concepts and techniques covered in the course.
Show steps
-
Identify the problem type and evaluation metric
-
Apply data resampling techniques
-
Build a model and evaluate its performance
-
Adjust the probability threshold to improve the evaluation metric
Create a machine learning model to identify imbalanced data.
Show steps
Provide hands-on experience building and evaluating machine learning models for imbalanced data.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning algorithm.
-
Train the model on the imbalanced dataset.
-
Evaluate the model's performance on a test set.
Code a simulated imbalanced classification data and try to predict the result
Show steps
Practice coding simulated imbalanced classification data to solidify understanding of the concepts in the course.
Show steps
-
Generate simulated imbalanced data using a library or framework
-
Split the data into training and testing sets
-
Build a classification model
-
Evaluate the model's performance using appropriate metrics
-
Tune the model's hyperparameters to improve performance
Create a blog post or presentation on imbalanced data classification
Show steps
By creating a blog post or presentation, you will have to synthesize and explain the concepts and techniques of imbalanced data classification, which will reinforce your understanding and enable you to share your knowledge with others.
Show steps
-
Choose a specific topic related to imbalanced data classification
-
Research and gather information on the topic
-
Organize and structure your content
-
Create your blog post or presentation
-
Share your work with others
Contribute to an open-source project related to imbalanced data classification.
Show steps
Provide hands-on experience working with real-world imbalanced data classification implementations and contribute to the open-source community.
Browse courses on
Open Source
Show steps
-
Find an open-source project related to imbalanced data classification.
-
Identify an issue to work on.
Develop a Python or R script for handling imbalanced data classification
Show steps
Create a practical deliverable that demonstrates your ability to handle imbalanced data classification in practice.
Show steps
-
Choose a dataset with imbalanced class distribution
-
Explore the dataset and understand the class imbalance
-
Implement data resampling techniques to address the imbalance
-
Build and train a classification model on the resampled data
-
Evaluate the model's performance using appropriate metrics
Participate in a Kaggle competition on imbalanced data classification.
Show steps
Combine practical application, problem-solving, and a competitive element to deepen your understanding of imbalanced data classification.
Browse courses on
Kaggle
Show steps
-
Find a Kaggle competition on imbalanced data classification.
-
Build a model for the competition.
-
Submit your model to the competition.
Review the book "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking"
Show steps
Provide a strong foundation in data science, data mining, and data analytic thinking, which are all key topics covered in this course.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first three chapters.
- Complete the exercises at the end of each chapter.
Follow online tutorials on imbalanced data classification
Show steps
Supplement your learning by following online tutorials that provide step-by-step guidance on the different aspects of imbalanced data classification.
Show steps
Show steps
Show steps
- Search for tutorials on imbalanced data classification
- Select tutorials that cover the specific topics you need to learn
- Follow the tutorials and complete the exercises
Join a study group or participate in online discussions
Show steps
Engaging in discussions and collaborating with peers will help you solidify your understanding, gain different perspectives, and identify areas where you need further clarification.
Show steps
Show steps
Show steps
- Find a study group or online discussion forum
- Participate actively in discussions
- Share your knowledge and insights with others
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Join a study group to discuss imbalanced data classification problems.
Show steps
Facilitate collaboration and knowledge sharing, helping you to better understand different perspectives on imbalanced data classification.
Show steps
Show steps
Show steps
- Find a study group.
- Participate in discussions.
Practice identifying imbalanced data classification problems.
Show steps
Prepare you to effectively handle imbalanced data, which is a common challenge in real-world datasets.
Show steps
Show steps
Show steps
- Find a dataset with imbalanced classes.
- Plot a histogram of the class distribution.
Complete online tutorials on imbalanced data classification techniques
Show steps
Access external references to further refine your knowledge of imbalanced data classification techniques.
Show steps
Show steps
Show steps
- Identify reputable online tutorials on imbalanced data classification techniques
- Complete the tutorials, taking notes and practicing the techniques
- Review the tutorials and practice the techniques regularly
Follow a tutorial on how to use SMOTE to oversample minority class data.
Show steps
Provide practical guidance on using SMOTE, a technique for oversampling minority class data to improve model performance.
Browse courses on
SMOTE
Show steps
Show steps
Show steps
- Find a tutorial on SMOTE.
- Follow the tutorial to implement SMOTE.
Solve practice problems
Show steps
Practice solving a variety of problems related to imbalanced data classification to improve your understanding of the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Identify the problem type and evaluation metric
- Apply data resampling techniques
- Build a model and evaluate its performance
- Adjust the probability threshold to improve the evaluation metric
Create a machine learning model to identify imbalanced data.
Show steps
Provide hands-on experience building and evaluating machine learning models for imbalanced data.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning algorithm.
- Train the model on the imbalanced dataset.
- Evaluate the model's performance on a test set.
Code a simulated imbalanced classification data and try to predict the result
Show steps
Practice coding simulated imbalanced classification data to solidify understanding of the concepts in the course.
Show steps
Show steps
Show steps
- Generate simulated imbalanced data using a library or framework
- Split the data into training and testing sets
- Build a classification model
- Evaluate the model's performance using appropriate metrics
- Tune the model's hyperparameters to improve performance
Create a blog post or presentation on imbalanced data classification
Show steps
By creating a blog post or presentation, you will have to synthesize and explain the concepts and techniques of imbalanced data classification, which will reinforce your understanding and enable you to share your knowledge with others.
Show steps
Show steps
Show steps
- Choose a specific topic related to imbalanced data classification
- Research and gather information on the topic
- Organize and structure your content
- Create your blog post or presentation
- Share your work with others
Contribute to an open-source project related to imbalanced data classification.
Show steps
Provide hands-on experience working with real-world imbalanced data classification implementations and contribute to the open-source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source project related to imbalanced data classification.
- Identify an issue to work on.
Develop a Python or R script for handling imbalanced data classification
Show steps
Create a practical deliverable that demonstrates your ability to handle imbalanced data classification in practice.
Show steps
Show steps
Show steps
- Choose a dataset with imbalanced class distribution
- Explore the dataset and understand the class imbalance
- Implement data resampling techniques to address the imbalance
- Build and train a classification model on the resampled data
- Evaluate the model's performance using appropriate metrics
Participate in a Kaggle competition on imbalanced data classification.
Show steps
Combine practical application, problem-solving, and a competitive element to deepen your understanding of imbalanced data classification.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a Kaggle competition on imbalanced data classification.
- Build a model for the competition.
- Submit your model to the competition.
Career center
Learners who complete Handling Imbalanced Data Classification Problems will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of imbalanced data classification techniques to build models that can accurately predict the minority class. This course will provide you with the skills you need to succeed in this role by teaching you how to select the best evaluation metric for imbalanced datasets and how to implement ROC curves and adjust probability thresholds to improve the selected evaluation metric of the model.
Data Scientist
Data Scientist
As a Data Scientist, you will use your understanding of various data resampling techniques like undersampling, oversampling, and SMOTE on imbalanced datasets to build classifiers that can identify or predict the minority class samples. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Data Analyst
Data Analyst
Data Analysts use their understanding of imbalanced data classification techniques to identify trends and patterns in data. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their understanding of imbalanced data classification techniques to identify business opportunities and risks. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their understanding of imbalanced data classification techniques to improve the efficiency of business operations. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Statistician
Statistician
Statisticians use their understanding of imbalanced data classification techniques to analyze data and draw conclusions. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Actuary
Actuary
Actuaries use their understanding of imbalanced data classification techniques to assess risk. This course will help you develop the skills you need to succeed in this role by providing you with a solid foundation in the theory and practice of handling imbalanced data classification problems.
Fraud Analyst
Fraud Analyst
Fraud Analysts use their understanding of imbalanced data classification techniques to identify and prevent fraud. This course may be useful for you if you are interested in a career in fraud analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their understanding of imbalanced data classification techniques to make investment decisions. This course may be useful for you if you are interested in a career in quantitative finance.
Risk Manager
Risk Manager
Risk Managers use their understanding of imbalanced data classification techniques to identify and manage risks. This course may be useful for you if you are interested in a career in risk management.
Market Researcher
Market Researcher
Market Researchers use their understanding of imbalanced data classification techniques to identify trends and patterns in consumer behavior. This course may be useful for you if you are interested in a career in market research.
Insurance Analyst
Insurance Analyst
Insurance Analysts use their understanding of imbalanced data classification techniques to assess the risk of insurance claims. This course may be useful for you if you are interested in a career in insurance analysis.
Credit Analyst
Credit Analyst
Credit Analysts use their understanding of imbalanced data classification techniques to assess the creditworthiness of borrowers. This course may be useful for you if you are interested in a career in credit analysis.
Epidemiologist
Epidemiologist
Epidemiologists use their understanding of imbalanced data classification techniques to study the causes and spread of disease. This course may be useful for you if you are interested in a career in epidemiology.
Social Scientist
Social Scientist
Social Scientists use their understanding of imbalanced data classification techniques to study social phenomena. This course may be useful for you if you are interested in a career in social science research.
For more career information including salaries, visit:
OpenCourser.com/course/tbve55/handling
Reading list
We've selected seven 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
Handling Imbalanced Data Classification Problems.
This classic textbook provides a comprehensive overview of statistical learning methods, including linear and logistic regression, support vector machines, and decision trees. It provides a strong foundation for understanding the concepts and techniques used in imbalanced data classification problems.
Provides a comprehensive overview of data mining concepts and techniques, making it a valuable resource for beginners in the field. It covers topics such as data preprocessing, feature selection, and model evaluation, which are essential for handling imbalanced data classification problems.
Provides a hands-on introduction to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, feature engineering, and model evaluation, providing a practical understanding of the skills needed to handle imbalanced data classification problems.
Provides a comprehensive overview of deep learning concepts and techniques using the Python programming language. While it does not specifically cover imbalanced data classification problems, it offers a valuable overview of the field and provides a foundation for further exploration.
Provides a comprehensive overview of machine learning concepts and algorithms using the R programming language. While it does not specifically cover imbalanced data classification problems, it offers a valuable overview of the field and provides a foundation for further exploration.
Provides a comprehensive overview of machine learning concepts and algorithms using the Python programming language. While it does not specifically cover imbalanced data classification problems, it offers a valuable overview of the field and provides a foundation for further exploration.
Provides a practical introduction to data science concepts and techniques for business applications. While it does not specifically cover imbalanced data classification problems, it offers a valuable overview of the field and provides a foundation for further exploration.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tbve55/handling
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Bhaskarjit Sarmah
Language
English
Good to know
Offers hands-on labs and interactive materials, which can help learners put their newfound knowledge to the test and gain a better understanding of how to handle imbalanced data classification problems
Taught by recognized experts in the field, Bhaskarjit Sarmah, ensuring learners are being taught by industry-leading professionals
Builds a strong foundation for beginners in the field of imbalanced data classification problems, providing a solid base of knowledge and skills to build upon
Develops professional skills and deep expertise in handling imbalanced data classification problems, making it ideal for those looking to advance their careers or skillsets
Provides a comprehensive study of strategies for handling imbalanced data classification problems, ensuring learners gain a well-rounded understanding of the topic
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