Now we've got a framework we can use for machine learning problems.
It's time to start matching the framework to existing data science and machine learning tools.
We're going to start right at the foundation says machine learning is quite broad.
There are many different tools that we could use.
So we need something to help manage our tools for us to do so.
You can use Anaconda many condo and condo and now I know you might be thinking snakes.
What the hell is Daniel talking about.
But trust me once you understand these setting up future machine learning projects such as ones which
use tools like you see here are going to be a breeze.
You can think of Anaconda and many Conda as the hardware store and workbench of data scientists and
Conda as your personal assistant Anaconda and many conduct are software distributions.
So much like when you download an app from the App Store.
It comes with code.
Other people have written which you can use on your computer or smartphone.
The same goes for these two.
They come with code.
Other people have written which we can take advantage of on our computers for our own machine learning
projects collections of code written by other people are often referred to as packages or tools.
For example for our heart disease classification problem we've talked about we might want to use several
Python packages each of which could be considered a different tool for different stages of the project.
In the beginning we might use pandas and map plot leave packages for data analysis and data exploration
then to build a machine learning model we might use the psychic loan package it might use.
All of these packages within a Jupiter notebook which comes from the Jupiter package now downloading
Anaconda into your computer requires around three gigabytes or so of space and it installs all of the
major data science and machine learning packages.
However remember how I said Anaconda is like the hardware store of data science tools.
This means it's got almost every data science tool you can imagine.
And from my experience many of them don't get used.
And since we're focused on being practitioners we're going to be using the most useful ones.
This is where many contact comes in.
Many come to remember is that the data scientists workbench it starts with the minimum requirements
to get started such as Python and a few others.
And now because of this it takes up up to about 10 times this space on your computer.
Coming in and around 200 megabytes so to save space and to get started we're going to use many Conda.
But I mentioned Anaconda because I want you to be aware of it.
If someone mentions it or you see it somewhere online.
Up next we're going to look at Conda which is like your assistant that comes with many conduct.
So once you download many conduit we'll have a few tools but then you use Conda to help manage those
tools for you.
We'll look into this more in a second.