Welcome back.
So up until now, we've learned the steps of machine learning.
And we learned the theory about machine learning.
The last piece of the puzzle is what tools do we use in order to learn about machine learning so that
we can finally start doing some exercises and projects?
And this is why Python is actually one of the most popular languages when using machine learning, because
Python has such a big community and it's such an easy, expressive language as we've learned that it
has a ton of libraries and modules that really help us do machine learning in a really fast, efficient
way.
It's not that Python is just the only language that you can do machine learning in.
It's that Python has the best tools available currently to do machine learning.
So what are these tools?
The first one is NumPy.
And numpy is definitely a must when it comes to machine learning.
It's a library that helps us to use lists and arrays in Python, and specifically multidimensional arrays
which are used very, very often in machine learning.
So we're going to be using NumPy.
And then the next one is pandas.
And pandas is used for what we call data analysis.
PANDAS gives us an idea of this data frame, which allows us the functionality to manipulate data in
tabular structure with rows and columns.
That is, we get to usually read a CSV file using pandas.
It's going to extract this data into something called data frame, and you can do all these amazing
transformations and aggregations using these data frames.
And don't worry, I'll show you how to do this in the next video.
So we have NumPy for working with lists and arrays, pandas for usually working with something like
tabular data, so CSV files.
And then we have a psychic learn and psychic learn.
Is that really exciting?
Create a model part where we import the psychic library and psych.
It comes prebuilt with, as you can see, different algorithms that we can use.
So we can do classification, we can do regressions, we can do clustering just by importing this library.
And then we use something like mad plot lib.
Now map plot lib again is another library, what we call a charting library.
It allows us to visualize data in a nice way.
And there's many tools that are built on top of maps lib that help us visualize data in any different
way that we want.
Finally, the two last crucial tools that we need is, first of all, Jupyter Notebooks.
If you remember in the developer environment section, we talked about Anaconda and how Anaconda, by
installing it, gives us access to the Jupyter notebooks.
In Jupyter notebooks are really, really useful because up until now we've written code in Pi Charm
or Sublime Text or Visual Studio code.
But Jupyter Notebooks are really, really useful when we do machine learning or data science because
as we'll see, it allows us to step through our code and actually output and keep track of all these
things that we've been doing.
And also see visualizations, something that you can't really do in a text editor like sublime text
or pie chart.
So you can step through each data analysis stage and have these notebooks visualized and potentially
even give to different people in your company so that you can create a story and share it with others.
So it's a notebook that allows us to communicate better.
And it's a very critical tool that is pretty much standard now in the data science and machine learning
field.
And as you can see, these are crucial tools in our tool belt that we're going to use throughout the
next couple of videos.
But all of them work really, really well with Python, and it's why Python has become such a standard
in the machine learning community.
The final piece of the puzzle, though, is that data right?
When we import the data and clean the data, we talked about the most difficult part being the actual
collection of the data.
In order for us to learn about machine learning, we actually need some data to work with.
Now we're not Facebook or Amazon or Google or Apple, but there's another useful tool called Kaggle.
And Kaggle is a community of machine learning experts where you can do competitions, you can look at
other people's Jupyter notebooks, but it also has data sets.
That is, we have free access to data, to massive amounts of data that are openly accessible.
If you want to learn about crimes in Boston, about the PGA Tour, about gas prices in Brazil, you
can actually download for free these data sets so that you can play around with machine learning.
And we're going to do just that in the next video.
So for now, make sure if you haven't, get your Jupyter notebooks ready again.
Check the developer environment section where I show you how to set this up and then create a free account
on Kaggle because we're going to start getting our hands dirty with some coding.
I'll see in the next one.
Bye bye.