Introduction to TensorFlow and Keras for Machine Learning

Machine learning can feel overwhelming with complex mathematical formulas and scattered APIs, but modern frameworks make it highly accessible. This text-based course guides you through TensorFlow and Keras, helping you understand how to structure, train, and evaluate neural networks from the ground up. You will transition from understanding core machine learning concepts to writing clean, production-ready TensorFlow code. By studying practical code snippets and clear written explanations, you will gain the confidence to design custom data pipelines and build neural networks for real-world regression and classification tasks. What you'll learn: - Understand foundational machine learning concepts and TensorFlow's API hierarchy. - Build robust input data pipelines using tf.data.Dataset for diverse data formats. - Configure and transform numeric, categorical, and hashed feature columns. - Create deep learning models using both the Keras Sequential and Functional APIs. - Apply activation functions, loss metrics, and optimizers to train models effectively. - Implement modern best practices in model evaluation and clean Python coding standards. The course starts with key terminology, basic concepts, and foundational definitions before moving into practical pipeline construction. You will then progress through building basic regression models, culminating in designing and training multi-layered deep learning architectures. This course is designed for beginners who want a clear, conceptual, and text-based introduction to machine learning using TensorFlow. No prior experience with deep learning is required, though a basic understanding of Python is helpful. Start reading today to build your foundation in modern machine learning.