Build Sarah – An Image Classification AI
Code an AI that is trained to detect and classify vehicles, animals, and other objects as you learn the core concepts underlying image classification and machine learning.
- 2.9 Hours of Video
- Certificate of Completion
- Source Code
- Closed Captions
- On-demand, 24/7 access
- 2.9 hours of video
- Certificate of completion
- Source code and PDF notes
- Closed captions
This course has been deprecated and is no longer supported. For our latest courses on computer vision, consider subscribing to our evergreen curriculums with a subscription package.
In this course we’ll be building an image classification Artificial Intelligence (AI) that can detect transportation vehicles, animals and other objects. We’ll train our AI with a dataset of images, and then give it new images which it will be able to classify.
We’ll begin by introducing the concept of image classification and Machine Learning. We’ll the cover the Nearest Neighbor Classifier technique and write a script to implement it. Then, we’ll move on to covering the k-Nearest Neighbor technique which provides a more generic approach to solving this problem. Parameters of our AI will be discussed and we’ll use Hyperparameter Tuning for that. To train our image we’ll use the CIFAR-10 Image Classification Dataset, which is broadly used in academia and the industry.
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- Intermediate knowledge of using NumPy
- Intermediate knowledge of Python 3
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