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Learn the complete process of building machine learning-powered products from conception to deployment in this comprehensive course spanning over 11 hours. Master the fundamentals of neural networks using PyTorch and PyTorch Lightning, then advance to modern architectures including convolutional neural networks and transformers. Develop proficiency in essential ML infrastructure and tooling, including experiment management systems and development workflows. Acquire critical skills in troubleshooting ML models, implementing proper testing methodologies, and managing data pipelines effectively. Explore data annotation techniques and best practices for maintaining high-quality datasets. Gain hands-on experience deploying ML models to web environments and implementing monitoring systems for production applications. Understand continual learning approaches to keep models updated and relevant over time. Examine the latest developments in foundation models and their practical applications. Learn how to effectively manage ML teams and projects, including workflow optimization and collaboration strategies. Address ethical considerations in machine learning development and deployment, ensuring responsible AI practices throughout the development lifecycle.
