Building Linear Regression Models with PyTorch and Autograd

Linear regression is the absolute foundation of machine learning, and implementing it from scratch is the best way to understand how deep learning libraries actually work. This course demystifies the core mathematical principles and translates them into clean, readable code. Through clear written explanations and step-by-step code walkthroughs, you will learn to construct, train, and evaluate linear regression models. You will gain a thorough understanding of computational graphs and hardware-accelerated tensor operations. What you'll learn: - Understand the fundamentals of PyTorch tensors and how to manage data on both CPU and GPU. - Master the Autograd engine to automatically compute gradients for model parameters. - Build a complete linear regression pipeline from mathematical formulation to working code. - Configure loss functions and optimization algorithms to iteratively improve model accuracy. - Apply model evaluation techniques to measure and interpret prediction errors. You will begin by learning key terminology and essential tensor operations before assembling the training loop. The material progresses logically from data preparation and forward passes to loss computation and backpropagation. This course is ideal for beginners in data science and software development who want a clear, conceptual introduction to PyTorch without any complex prerequisites. Start reading now to master the building blocks of modern machine learning.