Machine Learning with Keras: Build, Train, and Deploy on the Cloud

Transitioning from writing machine learning code on your local machine to deploying scalable models in production is one of the most critical steps for modern developers. Keras makes building deep learning models highly intuitive, but scaling and deploying them on cloud infrastructure requires a structured, step-by-step approach. This text-based course guides you through the entire lifecycle of a machine learning project. You will transition from understanding fundamental neural network concepts to training, optimizing, and deploying production-ready models on cloud platforms. What you'll learn: - Understand the foundational principles of deep learning, neural networks, and the Keras API. - Build and train robust machine learning models using TensorFlow and Keras. - Optimize model performance by tuning hyperparameters and improving training accuracy. - Deploy trained models to cloud-based ML services for scalable, real-world predictions. - Apply modern MLOps principles to automate and monitor your model deployment pipelines. You will start with core terminology and basic neural network architectures before moving into practical model creation. The curriculum then guides you through training techniques, model evaluation, and deployment strategies using modern cloud machine learning services. This course is designed for beginners, aspiring data scientists, and developers looking to enter the world of cloud-based machine learning. No prior experience with deep learning or cloud architecture is required. Begin your journey into cloud-scale machine learning today.