Image classification is critical across industries but often requires time-intensive manual analysis. This course will teach you to build and deploy convolutional neural networks for real-world image recognition tasks.

What you'll learn

Image classification tasks across industries - from healthcare to manufacturing - often require time-intensive manual analysis that is prone to variability and delays. In this course, Build an Image Recognition Model, you'll gain the ability to develop and deploy convolutional neural networks for real-world image classification tasks.

First, you'll explore how to prepare and preprocess image datasets, including normalization, augmentation, and proper train-test splitting. Next, you'll discover how to build CNNs from scratch and leverage pretrained models through transfer learning. Finally, you'll learn how to evaluate model performance using confusion matrices, accuracy metrics, and other diagnostic tools to ensure reliable predictions.

When you're finished with this course, you'll have the skills and knowledge of deep learning for image recognition needed to build production-ready models that can assist in medical diagnosis, quality control, and other computer vision applications.

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