Can anyone describe about training and testing data more clearly with an example?
I will say that Training is an organized process of imparting information and instruction to someone with the ultimate purpose of improving the person's knowledge, skills,and performance.
I don't know what you are looking for about testing data.
I think you need to be specific about the data testing you are looking for. It will give us more clue as to the suggestion to make towards your questions.
Are testing primary data or secondary data.
Machine learning is a subfield of computer science that gives the computer the ability to learn without being explicitly programmed (Arthur Samuel, 1959). For this to happen, a machine needs to be "trained" by explicitly feeding it data that has the correct answers attached. This training data will help the machine to connect the patterns in the data to the right answer. Once trained in this way, a machine can now be given test data that has no answers. The machine will then predict the answers based on the training it received.
Most data scientists divide their data (with answers, that is historical data) into three portions: training data, cross-validation data and testing data. The training data is used to make sure the machine recognizes patterns in the data, the cross-validation data is used to ensure better accuracy and efficiency of the algorithm used to train the machine, and the test data is used to see how well the machine can predict new answers based on its training.
Training data is used to adjust the parameters of the model, e.g. NN architecture. The aim is to reduce bias or your predictions (i.e. to fit the data).
The validation set (also called dev set) is applied for hyper-parameter tuning (regularization, early stopping, drop-off, learning rate...) to reduce variance, i.e. improve the generalization capacity of your model (eliminate over-fit). The validation data is not seen by your model during training.
Test data is used to provide an unbiased evaluation of a final model. It is not seen by your model at all. Test data should be your real-life data.
Test data and validation data should come from the same distribution. For train data you can you various data augmentation techniques. https://www.coursera.org/learn/deep-neural-network
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