Summary An Overview of Machine Learning: Techniques, Applications, and Challenges
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Course
Machine learning
Institution
Machine Learning
My document is an overview of machine learning, a subfield of artificial intelligence that involves training computer algorithms to learn patterns in data and make predictions or decisions based on that learning. The document covers various techniques used in machine learning, including supervised ...
The objective of this tutorial series is to give you a holistic understanding of
machine learning and how it works. Each step of the way with each of the major
algorithms we're going to cover a theory , application and then we 're going to dive in
deep to the inner workings of each of them. We 'll also be covering a healthy amount
of math here but we 'll be talking about the matting and explaining that as we go for
sure. Vladimir Vapnik came up with the support vector machine in 1963. The support
vector machines really took the lead for quite some time really up until very recently
when Google basically has kind of come back to really put some weight behind the
neural network specifically with deep learning. The first time we 've really been able
to really stretch and flex the muscles of machine learning up to this point it's really
been learning without the machine part. The first topic that we're gon na be covering
is regression and let 's go ahead and get into it. The series is for the people are really
looking to push the limits on what 's available so if you really just want to learn the
basics there already have some simple machine learning tutorials out there for just
applying machine learning to a dataset you can do this actually very very fast.
The idea of regression is to take continuous data and figure out a best fit line
to that data. So for example , a lot of people use regression with stock prices so that 's
what we are gonna do at least in this one. So with machine learning , basically
everything boils down to features and labels. You can either just download here or
more importantly here is the quandl code and then you can click on like python and
this is the exact statement to get it. If you have an account, you can make basically
unlimited request free data. Each column here is a feature. In machine learning you
can have all the features you want but you want to have meaningful features features
that actually have something to do with your data. A simple linear regression is not
gon na seek out that relationship. It 's just gon na work with whatever features you
feed through it so what we need to do is define those special relationships and then
use those as our features rather than redundant almost prices that not give us anything
else very useful first let 's do the high minus the low percent.
The next tutorial we 'll pick it up and start getting closer actually making real
predictions with this data. We define the only columns that we really care about and
so in our case the columns we care about are adjusted close , the high low percent ,
the percent change and then volume. Volume is just how many trades were occurred
basically that day so volume is also kinda related to volatility so you can also make
more features.
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