Training data- Data that has been incorporated into the Machine Learning process at the beginning of the process. Commonly it’s labeled with the basics of intentional data outcome labels, so the users can be sure that the machine understands the annotated object correctly.
Test data- Data that will be incorporated later in a process. It contains unlabeled and never before used objects. The test data serves for getting the insight into machines’ prediction capabilities. If machines have understood the context of training data well, it will be easy for it to understand the context and complexity of the added test dana, and regarding it delivers valuable data outcome. Main characteristics of datasets are flexibility and size. Flexibility is related to a number of tasks that the machine is able to handle and execute. Size is related to the robustness of the data it can handle.
If you’re a beginner, try exploring the standard ML datasets such as Iris, CIFAR-10, and MNIS because they are already familiar and easy to be quickly loaded. If you are familiar with datasets operating, here is a list of a few worldwide recognized datasets resources
- Amazon datasets- Amazon’s registry contains a few datasets precisely classified in accordance with the field of applications like ecology, animals, satellite images, etc, and also listed some of the datasets publicly available and accessible on their servers.
- Googles Datasets Search Engine- Google is a world-famous search engine and they constantly helping and improving access to various datasets related to keywords that are assigned by users
- Kaggle- This company is well known for hosting deep learning and machine learning options and challenges. When they provide a dataset, at the same time you get a community of ML practitioners who should help you in leveling up your project progress.