Machine Learning and TensorFlow on Cloud Infrastructure

Modern data science requires more than just local scripts; it demands the power of the cloud to train and serve models at scale. This course provides a solid foundation in machine learning workflows, from initial data preparation to deploying professional-grade models in a cloud environment. What you'll learn: - Understand fundamental machine learning concepts and TensorFlow architecture - Prepare and preprocess large datasets for cloud-based training - Build and train neural networks using the Keras API within TensorFlow - Deploy models to production using modern cloud-native hosting services - Apply MLOps basics to manage model versions and performance tracking - Practice writing scalable code suitable for distributed computing environments Starting with core terminology and foundational definitions, the curriculum moves through data engineering essentials before diving into model development and cloud deployment strategies. This course is designed for beginners looking to transition from local experimentation to professional cloud-based machine learning. No prior cloud experience is required. Start your journey into cloud-powered machine learning today.