Regression From Scratch, Gradient Descent, 04B

🚀 Course: ML & AI for Mechanical Engineers 🔁 Module 04: Regression From Scratch 🧠 Lecture B: Gradient Descent 👇 Link to the notebook: https://tinyurl.com/264lt4ya 🚀 Gradient descent for linear regression helps you reduce prediction error by updating model parameters from scratch in Python. In this tutorial-style lecture, learn how error minimization, weight updates, loss landscapes, learning rates, epochs, and mean squared error work together to train regression models. You’ll see batch gradient descent and stochastic gradient descent implemented with NumPy and Matplotlib, using examples like mechanical deflection, apartment rent, delivery time, and beam data. Watch to understand learning curves, diagnose slow learning or divergence, and compare stable convergence across gradient descent methods. ▶️➕🔔 Don't forget to like, subscribe, and hit the notification bell! ⚠️ Disclaimer: AI Voice used. #tutorials, #gradientdescent, #errorminimization, #linearregression, #gradientdescent, #linearregression, #machinelearning, #linearregression, #losslandscape, #datavisualization, #batchgradientdescent, #linearregression, #numpy, #python, #sgd, #regression, #gradientdescent, #machinelearning, #numpy, #gradientdescent, #learningcurves, #numpy, #gradientdescent, #divergencedetection, #linearregression, #gradientdescent, #learningrate, #python,