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Instructor - Akarsh Vyas Welcome to Part 2 of our complete Machine Learning series. In this session, we dive deep into Supervised Learning, focusing on Regression Models – especially Linear Regression. This video is packed with theory, intuition, and hands-on implementation to help you build real predictive models. What you’ll learn: * What is Regression and where it's used * Linear Regression: Concept and Intuition * Light introduction to Cost Function and Gradient Descent * How to use Scikit-learn to implement Linear Regression * Model Evaluation Metrics: MSE, RMSE, R² Score * Train-Test Split: Why it matters * Understanding Overfitting and Underfitting with visuals * Hands-on Project: Predict House Prices using Linear Regression Whether you're a beginner exploring machine learning or a student brushing up your concepts, this video is designed to give you clarity, practical knowledge, and confidence to move ahead. Links: Suggestion - create your own structured notes. My notes 🥲 - https://drive.google.com/file/d/1K2uvS3IVpq6RTEqETPybTck-tLtXKQws/view?usp=sharing * Kaggle Notebook: https://www.kaggle.com/code/akarshvyas/notebook9fdd2dc0b8 * Collab notebook : https://colab.research.google.com/drive/1QlZkG9BSj_JHeqcjidEuVj-gTmwxLvBi?usp=sharing Don't forget to check out Part 1 if you haven’t already.Like, share, and subscribe for more upcoming machine learning tutorials. 00:00:00 - 00:01:27 intro 00:01:27 - 00:01:31 important note 00:01:31 - 00:02:58 intro 2 00:02:58 - 00:06:29 contents of the video 00:06:29 - 00:12:04 what is regression 00:12:04 - 00:38:47 linear regression 00:38:47 - 00:46:30 cost function 00:46:30 - 01:04:26 gradient descent 01:04:26 - 01:10:59 repeat convergence theorem 01:10:59 - 01:14:56 hyperplane 01:14:56 - 01:28:46 project 01:28:46 - 01:46:17 y_test 01:46:17 - 01:57:37 overfitting and underfitting 01:57:37 - 02:40:45 project 2 02:40:45 - 02:41:17 outro
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