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Handling Imbalanced Data Classification Problems

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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.

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Enhance your skills in addressing imbalanced data classification challenges with this engaging and practical 2-hour project-based course. Designed for intermediate learners, this course dives into the nuances of understanding business problems and datasets pertinent to imbalanced data scenarios. Offered by Coursera, this personal development course provides valuable insights and techniques to tackle classification issues effectively. With free subscription options available, it's an excellent opportunity for data enthusiasts and professionals to advance their expertise in a crucial aspect of data science. Join now and take your data handling abilities to the next level!