TensorFlow and Keras on Cloud Platform

Taking machine learning models from local code to a scalable cloud environment can feel like a daunting step. This course bridges that gap by showing you how to design, train, and deploy neural networks using TensorFlow and Keras on modern cloud infrastructure. You will start by mastering the core principles of deep learning, progressing from basic neural network architectures to advanced model training. By the end of this text-based course, you will understand how to leverage cloud-based AI tools to train models on large datasets and deploy them as accessible APIs. What you will learn: 1. Understand the fundamentals of neural networks, tensors, and the Keras API structure. 2. Build and train deep learning models for classification and regression tasks. 3. Optimize model performance using hyperparameter tuning and modern regularization techniques. 4. Prepare and pipeline datasets efficiently for cloud-scale training. 5. Deploy trained TensorFlow models to cloud endpoints for real-time predictions. 6. Implement basic MLOps practices, including model versioning and monitoring on the cloud. The course begins with foundational machine learning terminology and Keras basics before guiding you through data preprocessing, model architecture design, and cloud deployment workflows. This course is designed for beginners in machine learning and cloud development. No prior experience with TensorFlow or cloud platforms is required, though a basic understanding of Python is helpful. Start your journey into cloud-based machine learning today.