Handling UnBalanced Datasets in Machine Learning Masterclass

 Handling of UnBalanced Dataset is a niche but a very important topic in the field of Data Science and Machine Learning.
 Handling UnBalanced Datasets in Machine Learning Masterclass
What you'll learn
  • Learn the state-of-the-art techniques and algorithms for handling Unbalanced Datasets
  • Understand variety of data based over sampling methods such as SMOTE, ADASYN, B-SMOTE and many more!
  • Understand variety of data based under sampling methods such as NearMiss2 , OSS and many more!
  • Apply Data-Based and Algorithmic Techniques in practice on Real Datasets
  • Learn strategies to help you avoid common problems when working with imbalanced dataset
 Data imbalance problem is recognized as one of the major problems in the field of machine learning as many real-world datasets are imbalanced. Learning from unbalanced data has many challenges and data professionals have to solve them in order to achieve good Accuracy and Metrics scores.The major problem is that models trained on imbalanced data sets strongly become biased toward the majority class and ignore the minority class.
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