Building Regression Models for Predictions in TensorFlow

Predicting continuous values—like housing prices, temperature, or financial trends—is a fundamental task in machine learning. This text-based course guides you through the process of designing, training, and evaluating regression models using TensorFlow.\n\nYou will transition from understanding basic mathematical concepts to implementing functional regression pipelines. By reading through clear, step-by-step explanations and structured code snippets, you will gain the confidence to prepare numerical data, configure model architectures, and optimize your model's predictive accuracy.\n\nWhat you'll learn:\n- Understand foundational regression concepts and key terminology before writing code\n- Configure model input layers and preprocess numerical features using modern TensorFlow APIs\n- Select and apply appropriate loss functions, such as Mean Absolute Error (MAE) and Mean Squared Error (MSE)\n- Train regression neural networks and monitor performance to prevent overfitting\n- Evaluate model predictions using standard validation metrics and basic tracking workflows\n\nThe course begins with the essential theory of regression and neural network inputs, then progresses to hands-on configuration of layers, loss functions, and training loops. You will wrap up by learning how to evaluate your model's real-world readiness.\n\nDesigned for beginners, this course requires only basic programming familiarity and no prior machine learning experience.\n\nStart reading today to build your first predictive regression models.