Imbalanced Learning (Unbalanced Data) – The Complete Guide

Learn how to handle imbalanced data in Machine Learning. Data based approaches, algorithmic approaches and more!

Created by: Bassam Almogahed
Machine Learning Specialist

What Will I Learn?

• Understand the underline causes of the Class Imbalance problem
• Why it is a major challenge in machine learning and data mining fields
• Learn the different characteristics of imbalanced datasets
• Learn the state-of-the-art techniques and algorithms
• Understand variety of data based methods such as SMOTE, ADASYN, B-SMOTE and many more!
• Apply Data-Based Techniques in practice
• Understand different algorithmic approaches such as: One Class Learning, Cost Sensitive Learning and more!
• Apply Algorithmic-Based methods in practice
• Learn how to correctly evaluate a prediction model built using imbalanced data
• Learn strategies and recommendations to help you avoid pitfalls when working with imbalanced dataset

Requirements

• Prior knowledge in machine learning/data science is necessary or at least currently enrolled in a machine learning course.

Target audience

• This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.