Foundations of Gradient Descent and Linear Regression

Understanding how machine learning models actually learn is the key to building successful AI applications. Instead of treating algorithms like a black box, mastering the foundational math of optimization allows you to troubleshoot, fine-tune, and build better models. This text-based course guides you through the core mechanics of gradient descent and linear regression, giving you a solid intuitive and practical understanding of how parameters update behind the scenes. You will transition from basic algebraic concepts to writing clean, vectorized optimization code. By focusing on the underlying mathematics and implementing algorithms step-by-step, you will build the confidence needed to transition to advanced deep learning frameworks. What you'll learn: - Understand the mathematical foundations of linear regression and cost functions - Implement gradient descent from scratch using NumPy to update model weights - Analyze the impact of learning rates and diagnose common optimization issues - Apply feature scaling techniques to accelerate model convergence - Practice vectorization techniques to write efficient, modern Python code - Prepare for deep learning by mapping manual updates to modern PyTorch concepts This course begins with essential terminology, defining features, targets, weights, and biases before diving into the mechanics of loss functions. You will then progress through the calculus of gradient descent, learning how to structure data for optimal training efficiency. This course is designed for beginners, aspiring data scientists, and software engineers who want a deep conceptual understanding of machine learning math. No prior machine learning experience is required, though basic Python familiarity is recommended. Start reading today to demystify machine learning optimization and build your mathematical foundation.