Machine Learning with TensorFlow on Cloud Platforms

Transitioning from local machine learning experiments to scalable, production-ready systems requires a solid grasp of both framework design and cloud deployment. This text-based course guides you from foundational machine learning concepts to deploying trained models in the cloud. You will understand how to structure your code, manage data pipelines, and leverage cloud scalability without needing prior machine learning experience. What you'll learn: - Understand core machine learning concepts and TensorFlow architecture from the ground up - Build and train predictive models using the high-level Keras API and efficient data pipelines - Deploy trained models to cloud infrastructure for real-time and batch predictions - Apply basic MLOps principles to monitor, version, and manage your model lifecycle - Configure cloud storage and compute resources to handle large-scale training workloads The course begins with essential terminology and mathematical intuition before walking through step-by-step written code implementations. You will progress from simple regression models to fully deployed cloud pipelines. Designed for beginner developers, data analysts, and aspiring engineers with basic Python knowledge. No prior machine learning or cloud experience is required. Start your journey into scalable machine learning today.