Скачать или смотреть Day 11 | Linear Regression Theory Complete Guide| Assumptions, Correlation, MSE, MAE, RMSE, R2 Score

In this video, we cover Linear Regression theory concepts in detail, including important assumptions and evaluation metrics used in Machine Learning.

📚 Topics Covered:

🔹 What is Linear Regression?
🔹 Assumptions of Linear Regression
 • Linearity
 • Independence
 • Homoscedasticity
 • Normality
 • No Multicollinearity

🔹 Coefficient of Correlation (r)
🔹 Mean Squared Error (MSE)
🔹 Mean Absolute Error (MAE)
🔹 Root Mean Squared Error (RMSE)
🔹 R² Score (Coefficient of Determination)
🔹 Adjusted R² Score

We also explain the mathematical intuition behind each metric and when to use them in real-world Machine Learning projects.

This video is perfect for:
✔️ BCA / BSc / MCA / Engineering Students
✔️ Data Science & Machine Learning Beginners
✔️ Interview Preparation
✔️ Competitive Exams

📌 By the end of this video, you will clearly understand how Linear Regression works and how to evaluate model performance using different error metrics.

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#MachineLearning #LinearRegression #MSE #RMSE #MAE #R2Score #DataScience #Statistics #MLTheory