Foundations of Neural Networks with TensorFlow and Keras

Are you ready to dive into the world of artificial intelligence but find deep learning concepts overwhelming? This course demystifies the fundamentals of neural networks and provides the practical skills needed to build functional models using the industry-standard TensorFlow framework. Upon completion, you will possess a solid understanding of how neural networks work and the ability to implement a complete machine learning workflow, from data preparation to model evaluation, using modern tools. What you'll learn: * Understand the foundational architecture of artificial neural networks (ANNs), including layers, weights, and activation functions. * Configure and implement deep learning models for classification and regression tasks using the high-level Keras API. * Apply essential data preparation techniques, including scaling, normalization, and handling structured data inputs. * Practice training models effectively by selecting appropriate loss functions, optimizers, and monitoring performance metrics. * Build a complete machine learning workflow, including saving and reusing trained TensorFlow models for deployment readiness. The course begins by establishing core terminology and concepts before guiding you through structured, practical implementation steps. You will focus on reading and understanding the code necessary to define, compile, and fit your first functional neural networks. This training is perfect for beginners with basic programming knowledge who want to enter the field of deep learning and AI. No prior machine learning or advanced math background is required. Start building your foundational knowledge in artificial intelligence today.