Machine Learning
Computer ScienceChapter 9 — Feature Engineering and Model Improvement
Lesson 9.4 : Handling Imbalanced Data
Reading & Interactive Lesson
Handling Imbalanced Data
Reading Material & Notes
Handling Imbalanced Data
This lesson explains Handling Imbalanced Data as part of Feature Engineering and Model Improvement in the Machine Learning course. It introduces the core concepts, terminology, workflow, and practical use of the topic.
Concept Overview
Understanding this topic helps learners connect fundamental theory with practical development and real-world applications. Study each concept carefully and practice it with small examples before moving to larger projects.
Important Concepts
- Understand the definition and purpose of the topic.
- Learn the important terminology and components.
- Understand the typical workflow or implementation steps.
- Identify practical use cases and common mistakes.
- Practice the concept with a small exercise.
Practical Example
/* Practice exercise: apply the Handling Imbalanced Data concepts here. */Learning Outcome
After completing this lesson, learners should be able to explain the main ideas of Handling Imbalanced Data, identify where they are useful, and apply them in an appropriate practical exercise.
Key Points
- Focus on understanding rather than memorization.
- Use practical examples to reinforce the concept.
- Review this lesson before proceeding to the next topic.
Course Curriculum
10 Chapters • 50 Lessons
Chapter 1 — Introduction to Machine Learning
5 Lessons
Chapter 2 — Types of Machine Learning
5 Lessons
Chapter 3 — Python for Machine Learning
5 Lessons
Chapter 4 — Data Preprocessing
5 Lessons
Chapter 5 — Regression Algorithms
5 Lessons
Chapter 6 — Classification Algorithms
5 Lessons
Chapter 7 — Clustering Algorithms
5 Lessons
Chapter 8 — Model Evaluation
5 Lessons
Chapter 9 — Feature Engineering and Model Improvement
5 Lessons
Chapter 10 — Machine Learning Projects
5 Lessons