Keras Machine Learning: Build, Train, and Deploy on Cloud Platform

Transitioning from writing machine learning code on your local computer to deploying scalable models in the cloud is a critical step for any developer. This text-based course bridges that gap by teaching you how to build, optimize, and deploy robust neural networks using Keras and Cloud Platform tools. You will start with the fundamental concepts of deep learning, learning how to structure data and configure neural network layers. By the end of this course, you will understand how to transition your local Keras models into fully operational cloud pipelines, ready to serve predictions at scale. What you'll learn: - Understand the foundational concepts of neural networks, tensors, and the Keras API. - Build and train deep learning models for regression and classification tasks. - Optimize model performance using hyperparameter tuning and regularization techniques. - Prepare and ingest large datasets efficiently using modern data pipeline practices. - Deploy trained Keras models to Cloud Platform endpoints for real-time predictions. - Apply modern MLOps principles to monitor, version, and manage your deployed machine learning models. The course begins with core machine learning definitions and basic Keras syntax before moving step-by-step through dataset preparation, model training, and cloud deployment strategies. You will read clear explanations and study practical code examples designed to build your confidence at every stage. This course is designed for beginners who have a basic understanding of Python and want to enter the field of cloud-based machine learning. No prior experience with deep learning or cloud platforms is required. Start reading today to build and launch your first cloud-ready machine learning models.