Animal species detection

Computer vision is a distinct field of machine learning, which, in turn, is a subset of AI. There are several beginner-level AI projects that you can develop using it, and one that we suggest is an animal species detection system. Here, you need to simply use images to train an ML model to understand the difference between various animals, such as dogs, cats, horses, hens, sheep, cows, and so forth. Technically, identifying animal species is a multi-class classification problem. Thankfully, being a beginner-friendly project, you can find several datasets on the web, such as Kaggle and GitHub . One dataset you can try is Animals-10, available on Kaggle. It has data on ten different animal species: butterfly, cat, chicken, cow, dog, elephant, horse, sheep, spider, and squirrel. If you want to focus on a Pythonic build, you can use a popular pre-trained model, VGG-16, and load it into Python using the Keras library. It is a CNN architecture trained on ImageNet that has more than 14 million images. Once you’ve successfully loaded the VGG-16 model, you can train it using the ML dataset that you chose earlier. For instance, if it was the Kaggle Animals-10 dataset, then you need to train VGG-16 to identify among ten different types of animals.