Semi-supervised, Unsupervised, and Reinforcement Learning Models
Explore semi-supervised, unsupervised, and reinforcement learning models to understand how AI systems identify patterns, handle limited data, and improve through trial and error. This lesson guides you through key algorithms like clustering and PCA and highlights applications in areas such as robotics and medical data analysis.
Unsupervised learning
If the data is unlabeled and we’re using machines to label the data and find patterns we don’t yet know of, it’s unsupervised. Effectively, we humans either know the right answer or we don’t, and that’s how we decipher which camp the ML algorithms belong to. As you might imagine, we take the results of unsupervised learning models with some hesitancy because it may be finding an organization that isn’t actually helpful or accurate.
Unsupervised learning models also require large amounts of data to train on because the results can be wildly inaccurate if it’s trying to find patterns out of a small data sample. As it ingests more and more data, its performance will improve and become more refined over time, but once again, there is no correct answer.
Applications of unsupervised learning models include clustering and dimensionality reduction. Clustering models segment or group data into certain areas. These ...