In gradient descent, a batch is the total number of examples you use to calculate the gradient in a single iteration
Stochastic gradient descent (SGD) takes this idea to the extreme--it uses only a single example (a batch size of 1) per iteration
Mini-batch stochastic gradient descent (mini-batch SGD) is a compromise between full-batch iteration and SGD. A mini-batch is typically between 10 and 1,000 examples, chosen at random
Mini-batch SGD reduces the amount of noise in SGD but is still more efficient than full-batch
I will continue to work on Google's Machine Learning Crash Course