Building Neural Networks with TensorFlow and Keras in Python

Have you ever wondered how machines learn patterns from data, but felt overwhelmed by the complex math of deep learning? Building your first neural network is straightforward when you use the right tools and approach. This text-based course guides you step-by-step from foundational artificial intelligence concepts to constructing your own fully connected neural networks. You will gain a clear, conceptual understanding of how layers, activation functions, and optimizers work together, enabling you to confidently train models that make predictions on real-world datasets. What you'll learn: 1. Understand the core terminology of deep learning, including neurons, weights, biases, and activation functions. 2. Configure neural network architectures using the Keras Sequential API and Dense layers. 3. Prepare and preprocess dataset structures using modern Python libraries. 4. Compile models with appropriate loss functions, optimizers, and evaluation metrics. 5. Train and evaluate your network while monitoring for overfitting using validation splits. 6. Apply modern best practices such as early stopping to improve model performance. You will start by exploring foundational theory and terminology before moving on to hands-on structural design. Through structured written explanations and clear code examples, you will progress from basic network configuration to compiling, fitting, and evaluating your models. This course is designed for beginners who have a basic familiarity with Python programming. No prior experience with machine learning or advanced mathematics is required. Start reading today to build a solid foundation in deep learning and create your first neural network.