.

Keeping this in view, what is a loss function in machine learning?

Loss functions and optimizations. Machines learn by means of a loss function. It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number.

Furthermore, how do you calculate loss in neural network? The loss is calculated using loss function by matching the target(actual) value and predicted value by a neural network. Then we use the gradient descent method to update the weights of the neural network such that the loss is minimized. This is how we train a neural network.

Also to know, what does a loss function do?

In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function.

What's a good MSE?

Long answer: the ideal MSE isn't 0, since then you would have a model that perfectly predicts your training data, but which is very unlikely to perfectly predict any other data. What you want is a balance between overfit (very low MSE for training data) and underfit (very high MSE for test/validation/unseen data).