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Throughout this specialization, learners will build advanced AI engineering skills by mastering topics like model tuning, optimization, convolutional and recurrent neural networks, transformers, and MLOps. You'll gain hands-on experience in hyperparameter tuning, building deep learning models, and using AI techniques such as transfer learning and fine-tuning to develop state-of-the-art AI solutions.
The specialization begins with an introduction to machine learning optimization techniques, focusing on hyperparameter tuning and cross-validation. From there, you’ll progress to building deep learning architectures using CNNs and RNNs for computer vision and sequence modeling tasks.
As you continue, you’ll explore advanced AI topics like transformer architectures and attention mechanisms, essential for modern NLP tasks. The specialization also covers MLOps practices, including deployment, containerization with Docker, and orchestration with Kubernetes.
This specialization is perfect for learners with a background in machine learning, deep learning, and Python programming. By the end, you will be able to implement hyperparameter tuning strategies and design CNNs and RNNs for AI tasks.
Applied Learning Project
Throughout this specialization, learners will apply their skills to hands-on projects involving model optimization, image classification, text generation, and NLP tasks like summarization and translation. These projects will allow you to practice working with real-world datasets and deploy AI models into production environments using tools like Docker and Kubernetes.
