📐 VC Dimension Explained with Examples | Module 1 Part 3 (KTU AMT305)

📌 **Welcome to Part 3 of the Machine Learning Module 1 series (KTU AMT305)!**
In this video, we explore one of the most fundamental theoretical concepts in Machine Learning – the **VC Dimension** (Vapnik-Chervonenkis Dimension). This concept helps in understanding the **capacity of a hypothesis class** and its ability to generalize from data.
🎯 **Topics Covered:**

✔️ What is VC Dimension in Machine Learning?
✔️ Importance of VC Dimension in Hypothesis Evaluation
✔️ Visual Intuition Behind Shattering
✔️ Examples: VC Dimension of Lines and Axis-Aligned Rectangles
✔️ VC Dimension and Model Complexity
👨‍🎓 **Who is this for?

* KTU AI & DS 5th Semester Students
* ML Beginners trying to understand theoretical foundations
* Anyone preparing for university exams or ML interviews
📘 **Subject:** Introduction to Machine Learning
📚 **Course Code:** AMT305 (KTU)
🎓 **University:** APJ Abdul Kalam Technological University
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Видео 📐 VC Dimension Explained with Examples | Module 1 Part 3 (KTU AMT305) канала KTU AI DATAVERSE