Now we figured out what problem we're trying to solve.
We've matched our specific problem to a different type of machine learning problem.
It's time to have a look at what data we have.
As you may have guessed the question we're trying to answer here is what kind of data do we have.
Data comes in many different shapes and sizes but the main two types are structured and unstructured
structured data is something you'd expect to see in an excel file such as rows and columns of different
patient medical records and whether or not they have heart disease or not or customer purchase transactions.
It's called structured data because all of the samples the different patient records are typically in
similar format meaning one column might contain numbers of a certain type such as average blood pressure
or sags or weight of a patient and another column might have whether they have chest pain or not and
what the level of intensity is.
Unstructured data are things like images natural language text such as transcribed phone calls videos
and audio files although we can turn these into numbers and create structure.
They typically come in many varying formats.
One picture of a dog may look completely different to another image of a dog and the email as you write
back and forth with the friend may have a completely different structure to the emails you'd write to
a co-worker.
Now within these two data types there's static and streaming data static data is data which doesn't
change over time.
You may have a spreadsheet of patient records in a dot CSP format which stands for commas Separated
Values which simply means all of the different data is in one file separated by commas.
It looks like this.
You check this table we got the idea.
Com I'll wait.
Comma sex and if we were to read that into it to a data frame using a tool like pandas we'll have a
look at this in a future lesson.
It would look something like this.
So a lot of data you'll actually come across comes in a simple format like this.
But to turn it into something that a little bit more structural you can convert it to this.
Now CSB is one of the most common types of static data formats.
We're going to get very used to this by the end of the course.
And since these values won't really change over time they're called static Usually what you'll want
is a lot of these examples in machine learning.
There's a saying The more data the better.
Which makes sense if you think about it the more examples you have of something such as the inputs and
outputs of patient records where the inputs are a patient's body parameters and the outputs are whether
they have heart disease or not.
The more chances you'll have to find patterns between them.
The same goes for machine learning algorithms.
The more examples they can look at the more chance they have at finding patterns and thus using those
patterns to predict something in the future.
Like whether a new patient who comes along who isn't in this table whether they have heart disease or
not.
Streaming data is data which is constantly changed over time.
For example say you wanted to predict how a stock price will change based on news headlines you'll be
working with streaming data.
Since news headlines are being updated constantly you'll want to be the first to see how they change
stocks most of the work you will do in practice will start on static data and then if your data analysis
and machine learning efforts prove to show some insights you'll move towards streaming data for when
you go to deployment or in production.
A common data science workflow begins by opening a v file in a Jupiter notebook a tool for building
machine learning projects then exploring the data and performing data analysis using pandas a python
library for data analysis and making visualizations such as graphs and comparing different data points
using map plot lib then building machine learning models on the data using psychic learn such as a machine
learning model to predict using these patterns here.
Whether or not a patient has heart disease don't worry if you're thinking what's a Jupiter notebook
and pandas what are we at the zoo.
We've got dedicated sections and projects coming out for each of these tools.
For now think about the different kinds of data you create or use every day.
Are they structured or unstructured.
How much data is there.