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Classify images using Convolutional Neural Network (CNN)
Updated on Sep, 2026
Programming, Data Science, Artificial Intelligence
Duration - 2.5 hours
Training 5 or more people ?
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Learn to build an image classification engine using a Convolutional Neural Network (CNN). CNN is a popular network where a machine can be trained to classify images based on patterns in the images. Once trained, it can be used to identify objects in the images.
A lot of smart researchers have already spent a lot of time building really good image classification networks like VGGNET, RESNET, and Inception V3. The networks are variants of CNN. These networks have been trained on an imagenet animal dataset. If your dataset requires a different type of image classification, you could just start with these networks and fine-tune them on your smaller dataset. This saves significant time and resources. Build a strong foundation in CNN with this course for beginners.
No prior knowledge of CNN or deep learning is assumed. I will be covering topics like deep learning, Convolution, and CNN from scratch.
Jobs in the computer vision area are plentiful, and being able to learn transfer learning will give you a strong edge. CNN is a state-of-the-art technology that can quickly help you achieve your goal.
Learning image classification with CNN will help you become a computer vision developer, which is in high demand.
This course teaches you how to build a dog vs cats classification engine using open-source Python and the Jupyter framework.
You will work along with me step by step to build the following answers:
Check out the detailed breakdown of what’s inside the course
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