Building Regression Models and L2 Loss in TensorFlow

Predicting continuous values—like housing prices, stock trends, or temperature changes—is a fundamental task in machine learning. Understanding how regression models work and how they optimize predictions is the key to building successful predictive systems. In this written course, you will transition from understanding basic regression concepts to building, training, and evaluating neural network regression models. You will explore how TensorFlow handles mathematical optimization using L2 loss and learn to make reliable predictions with your trained models. What you'll learn: Understand the core mathematical concepts behind linear regression and neural network layers; Implement regression architectures using the modern Keras API in TensorFlow; Configure L2 loss functions and understand how they penalize prediction errors during training; Optimize model weights using gradient descent and modern optimization algorithms; Evaluate model performance using standard metrics like mean absolute error and root mean squared error; Save and load your trained regression models for future predictions. You will start with foundational terminology and the basic math of regression before moving step-by-step through model construction, loss function configuration, and training loops. The course concludes with practical text-based exercises to test your model-building skills. This course is designed for beginners in machine learning and data science. No prior experience with neural networks is required, though a basic familiarity with Python programming will help you get the most out of the material. Start reading today to build your first predictive regression models with confidence.