Скачать или смотреть Math Behind Linear Regression | L1 & L2 Regularization Explained

📊 In this tutorial, we break down the mathematics of Linear Regression and introduce L1 & L2 Regularization, two powerful techniques for avoiding overfitting in machine learning models.

✅ What You’ll Learn:

Mathematics behind Linear Regression (slope, intercept, cost function)

Understanding Loss Function (MSE) in regression

What is Overfitting and why it happens

Introduction to Regularization in ML

L1 Regularization (Lasso Regression) explained with math

L2 Regularization (Ridge Regression) explained with math

Comparison between L1 & L2

Hands-on coding with scikit-learn

🎓 This video is part of the SMIT Data Science & Machine Learning Series, designed for beginners and intermediate learners.

👉 By the end of this video, you’ll understand how Linear Regression works mathematically and how L1 & L2 regularization improve model performance.

🔔 Subscribe for upcoming tutorials on logistic regression, decision trees, and advanced ML concepts.