Learning paradigms in ML
In this section, we will cover the differences between supervised, unsupervised, semi-supervised, and reinforcement learning and how all these learning categories can be applied. Again, the learning type has to do with whether or not you’re labeling the data and the method you’re using to reward the models you’ve used for good performance. The ultimate objective is to understand what kind of learning model gets you the kind of performance and explainability you’re going to need when considering whether or not to use it in your product.
Supervised learning
If humans are labeling the data (also known as structured data) and the machine is also looking to correctly label current or future data points, it’s supervised learning. Because we humans know the answer the machines are trying to arrive at, we can see how off they are from finding the correct answer, and we continue this process of training the models and retraining...
