Training Machine Learning Models with TensorFlow and Keras

Developing an intuitive understanding of how machine learning models learn is the first step toward building intelligent applications. This text-based course guides you through the core concepts of training, saving, and deploying neural networks without complex mathematical barriers. You will transition from understanding basic data flow to building functional predictive models. By reading detailed explanations and analyzing structured code examples, you will master the foundational mechanics of machine learning pipelines, from raw input data to trained model inference. What you'll learn: Understand the core terminology of machine learning, neural networks, and training loops; Build neural network architectures using Keras layers and TensorFlow operations; Prepare and preprocess structured data and images for efficient model consumption; Train machine learning models and monitor performance metrics to prevent overfitting; Save, export, and load trained models to establish a basic MLOps workflow; Apply trained models to make predictions on new, unseen data. The course begins with essential terminology, outlining how data moves through a processor during training. You will then progress through step-by-step written modules covering dataset preparation, model compilation, training execution, and final model serialization for production. This course is designed for aspiring developers, data enthusiasts, and beginners looking for a clear, written introduction to machine learning with no prior AI experience required. Start reading today to unlock the fundamentals of model training and build your machine learning foundation.