‫Why not pay?
‫Throughout this section, we're going to explore this popular library called NumPy, which are very
‫similar to Python lists.
‫It's one of the most used and popular libraries when it comes to data science and machine learning.
‫But I want to ask you, why do we need this?
‫Don't we have Python lists?
‫Why is NumPy so popular and a must use tool for machine learning?
‫There are two main reasons why we want to use numpy for our problems.
‫You see, under the hood, many functionalities that NumPy offers us are actually written in C, a programming
‫language that is really, really fast.
‫If we used Python lists, it would actually be a lot slower to do some of the things that we're about
‫to do.
‫So numpy operations on arrays and lists are a lot faster than performing the same operations in Python.
‫And as you'll find out when it comes to machine learning, we do a lot of computation.
‫We do a lot of processing.
‫And that's why GPUs or graphical processing units are used because so many calculations need to happen.
‫So we need to have any possible performance advantage we can, which NumPy offers.
‫The second reason we want to use something like NumPy is that when it comes to machine learning, well,
‫machines usually only understand ones and zeros and things like numbers which can be contained in numpy.
‫Are really easy for machines to understand versus something more complicated.
‫For example, an image can be represented in numbers where a number represents the color of a pixel.
‫And we'll see later on in the course where we try and convert different fields, different attributes
‫into numbers.
‫For example, if a patient has heart disease instead of yes or no, we convert it to one or zero.
‫This idea of converting data into something that a machine can understand, like numbers like binary,
‫like ones and zero allows us to make machines learn.
‫That's how machines learn.
‫Machines learn by performing multiple operations and by storing this information in an array like numpy
‫is a great way for us to represent that data from what humans can understand to what computers can understand.
‫But you'll see that as we progress more and more through the course.
‫For now, let's get started.