Building Regression Models with TensorFlow

Predicting continuous numerical values is a cornerstone of practical machine learning, driving key decisions from financial forecasting to trend analysis. This text-based course guides you step-by-step through the process of building, training, and evaluating regression models using TensorFlow. You will transition from understanding basic mathematical concepts to writing clean, modern TensorFlow code that solves real-world regression problems. By learning how to prepare datasets, structure neural networks, and prevent overfitting, you will gain the confidence to tackle predictive analytics independently. What you'll learn: - Understand core regression concepts and foundational machine learning terminology. - Build linear regression models using the modern TensorFlow and Keras API. - Implement polynomial regression to capture complex, non-linear relationships in data. - Apply loss functions and optimizers specifically designed for continuous value prediction. - Evaluate model performance using standard metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE). - Practice debugging and tuning model parameters through written exercises and code walkthroughs. The course begins with key terminology, basic concepts, and foundational definitions before moving into practical code structures and model tuning. Designed for beginners with basic Python knowledge who want to start their machine learning journey with TensorFlow, this course requires no prior machine learning experience. Start reading today to build your first predictive models with TensorFlow.