Welcome back. 1
Another day, another machine learning module. 2
In this lesson I want to take a brief minute to talk about what we're going to do in this section. 3
So far we've covered one machine learning technique, namely linear regression. 4
And this was with one variable only. And 5
also, we worked through the fundamentals of the Python programming language. 6
So where do we go from here? 7
Well, before we dive deeper into the next projects and tackle more complex machine learning techniques, 8
we've got to peek under the hood and kind of get an understanding of how machine learning actually works. 9
I mean, how does the learning in machine learning actually happen? 10
What are the steps involved and how is it that we can give our computer a whole bunch of data and get 11
back these beautiful parameter estimates? 12
How are these calculations actually made? 13
And to answer these and other utterly fascinating questions, we're going to have to get our hands dirty 14
with some mathematics and some algorithms. And this means getting comfortable and working through a couple 15
of difficult concepts. 16
And that includes this idea of optimization, mathematical optimization. 17
So you've probably already guessed that this section is going to be a bit more technical than the previous 18
modules. 19
We're going to be doing some calculus. 20
We're going to work with derivatives and partial derivatives. 21
We're going to be doing some more advanced programming Python too, because we're gonna be using loops and 22
we're going to write our own optimization algorithm from scratch. And the optimization algorithm in question 23
is called Gradient Descent. 24
And once we've done all that, we're gonna apply this algorithm to various problems and see how it behaves. 25
And what I mean by that is - in what situations does the algorithm do well? And when does this algorithm 26
fall flat on its face? 27
What are the strengths and weaknesses of the approach? 28
What is the algorithm sensitive to? 29
And does our algorithm have an allergy to peanuts? And all this will help us build an understanding of 30
how machine learning actually works from the ground up. 31
And if we want to be able to understand the more complex machine learning techniques like neural networks 32
and tackle the more difficult projects, we're going to have to build up an understanding of how these 33
techniques work to arrive at a solution in the first place. 34
And that about covers the intro. 35
I'll see you in the next lesson.