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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