Foundations of Gradient Descent: Initialization and First Predictions

Every journey into machine learning begins with understanding how models learn from data. At the core of this learning process is gradient descent, the fundamental optimization algorithm used to train modern artificial intelligence. This text-based course guides you through the absolute starting points of gradient descent, helping you transition from understanding basic mathematical concepts to writing clean Python code to initialize model parameters and calculate initial predictions. What you'll learn: - Understand the core mathematical concepts behind linear regression and optimization - Initialize model parameters randomly using modern NumPy practices - Compute initial model predictions using vectorization for efficiency - Analyze the role of weights and biases in shaping model outputs - Practice step-by-step calculations through guided written exercises The course starts with fundamental definitions of parameters and loss functions before moving to hands-on calculations of your first predictions. You will read clear explanations, study structured code snippets, and complete written exercises to solidify your understanding of the mechanics of model training. This course is designed specifically for beginners, requiring only basic algebra and introductory Python knowledge, with no prior machine learning experience needed. Start building your machine learning foundation today by mastering the mechanics of gradient descent.