Machine Learning Frameworks: Building Algorithms with Keras

Deep learning is transforming how we solve complex data challenges, yet getting started with neural networks can feel overwhelming. This course simplifies the process, guiding you from basic machine learning concepts to building your own functional models using Keras. You will understand how to structure neural networks, manage datasets, and tune model performance effectively. By the end of this course, you will have a solid grasp of deep learning workflows, enabling you to build, train, and evaluate your own neural networks for classification and regression tasks. You will also learn how to leverage modern practices like transfer learning and model saving for production-ready workflows. What you'll learn: - Understand the core concepts of neural networks, layers, activation functions, and optimizers - Configure and compile deep learning models using the intuitive Keras API - Prepare and preprocess structured data for training and validation - Train neural networks and monitor performance to prevent overfitting - Apply transfer learning techniques to build on top of pre-trained architectures - Save, load, and prepare your trained models for deployment Starting with fundamental deep learning terminology and architectural basics, the course moves step-by-step through designing network layers, compiling models, and evaluating training runs with practical, text-based examples. This course is designed specifically for beginners with basic Python knowledge who want to enter the field of artificial intelligence. Start reading today and build your first neural network with confidence.