Linear Regression From Scratch in Python | Gradient Descent Explained Step-by-Step

Welcome to Pytechie🚀 In this video, we build Linear Regression from scratch using Python and NumPy while understanding the complete mathematics behind Gradient Descent and machine learning optimization. If you're preparing for Machine Learning interviews, learning ML fundamentals, or want to understand how algorithms work internally without relying on libraries like Scikit-Learn, this tutorial is for you. 📌 What You’ll Learn: ☑️ What Linear Regression actually does ☑️ Understanding the line of best fit ☑️ Mean Squared Error (MSE) explained ☑️ Cost function intuition ☑️ Gradient Descent step-by-step ☑️ Derivatives for slope and intercept ☑️ Updating parameters using gradients ☑️ Training Linear Regression from scratch ☑️ Visualizing regression line and loss curve ☑️Evaluating performance using: ✅ MSE ✅ RMSE ✅ MAE ✅ R² Score ☑️Predicting unseen data 💻 Technologies Used: ☑️ Python ☑️ NumPy ☑️ Matplotlib 🎯 Perfect For: ☑️ Beginners in Machine Learning ☑️ Python developers ☑️ Data Science students ☑️ ML Interview preparation ☑️ Anyone wanting to learn ML from scratch Github Code: https://github.com/Pytechie-dev/Interview/blob/cbd70ff287a21d7accfa94bbe7b169d8794c83b5/linear_regression.ipynb 🔥 In this series, we will implement machine learning algorithms completely from scratch to deeply understand the core concepts behind AI and Data Science. 📌 Don’t forget to: 👍 Like the video 💬 Comment your doubts 🔔 Subscribe to Pytechie for more ML and Python tutorials #machinelearning #linearregression #python