Hey everyone!
I just published a beginner-friendly guide on Simple Linear Regression where I cover:
- Understanding regression vs classification
- Why “linear” matters in the algorithm
- Error minimization explained in plain English
- A hands-on Python project with code, visuals, and predictions
It’s designed for anyone just starting out in ML who wants to learn by building — without drowning in heavy math or abstract theory.
If you get a chance to read it, I’d love your feedback, comments, and even an upvote if you find it useful. Your support will help more beginners discover it!
Blog Link: Medium
Code Link: Github
submitted by /u/Motor_Cry_4380
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