‫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.
‫Since 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, mini condor and Condor.
‫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 Mini Conda as the hardware store and workbench of data scientists.
‫Anaconda as your personal assistant.
‫Anaconda and many Condor 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 too.
‫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 lib packages for data analysis and data exploration.
‫Then to build a machine learning model, we might use the psychic loan package.
‫We might use all of these packages within a Jupyter Notebook, which comes from the Jupyter 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 we 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 conduct.
‫Remember, it's like the data scientist 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 ten times less space on your computer.
‫Coming in at 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 conduct, which is like your assistant that comes with many conduct.
‫So once you download Marie Kondo, it'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.