Ready to transform your approach to machine learning with imbalanced data? Join our comprehensive course and discover powerful techniques to improve model performance on datasets where class imbalance challenges traditional algorithms.
What You'll Learn:
1️⃣ Evaluation Metrics: Discover specialized metrics like ROC-AUC, F1-score, imbalanced accuracy, and more, tailored for imbalanced data.
2️⃣ Resampling Techniques: Master under-sampling, over-sampling, SMOTE, and their advanced variations to balance your datasets. Understand how they work and also their LIMITATIONS.
3️⃣ Cost-Sensitive Learning: Learn to assign class weights and penalize misclassifications effectively.
4️⃣ Ensemble Methods: Explore bagging and boosting algorithms crafted for imbalanced classification.
🧰 Tools & Techniques:
Implement these methods using Python libraries like Imbalanced-Learn and Scikit-learn, ensuring you're industry-ready.
Whether you're tackling fraud detection, medical diagnosis, or any other domain with skewed data, this course equips you with the skills to handle imbalance with confidence. Enroll today and transform your models!
Enroll now: https://www.trainindata.com/p/machine-learning-with-imbalanced-data