linear algebra week1 introductio

Q1:If we just think about energy alone for a moment, our society literally lights up our planet from space.(what's the meaning of it?)

All of these questions we could answer better, if we could analyze the energy and usage data we had better.

If we had good models of data, then we could also make predictions.Practically speaking, we could build software that tells you how to take the fastest journey across your city and put in a map.And the process by which we optimize those network is called Machine Learning.

Now, it turns out that linear algebra, how to solve systems of equations, like this where the variables obey the rules of vectors like these and then taking those forward and turning, recasting them as matrices like these objects here.

But one of the problems is that, most courses on data science and machine learning presuppose a level of familiarity with linear algebra and calculus that not everybody has.

Depending on how it was taught, it may not be at all obvious to you why that maths would even apply to the world of data.(linear algebra and calculus).We're just aiming to very quickly and in a way strongly both motivated by machine learning problems, give you enough underpinnings to get you going.

And more than anything else, we want to develop your mathematical intuition.

So, linear algebra is defined to be, the study of vectors, vector spaces, a mapping between vector spaces.It emerged from the study of systems of linear equations and the realization that these could be solved using matrices and vectors.

So the first thing we'll do, is look at vectors and the operations we can do with vectors and then we'll move on to look at matrices.