Hi! Welcome to the python programming for machine learning series. Python is extremely powerful,
easy to use, versatile, and has great readability, meaning the syntax is not confusing or misleading,
which makes it a great first programming language. And as a benefit to AI, it has several programming
libraries linked to it that are very useful for Machine Learning and Data Science. In this series,
we’ll be looking at the 4 most common and important python libraries for ML: Numpy,
Pandas, Matplotlib, and Seaborn. Now as a quick side note, this series won’t go into the very
basics of the Python programming language itself – mostly basics of Python for these 4 libraries.
That said, you won’t have to be an expert on Python to understand the content in these videos,
but a little background knowledge on programming as a whole might be helpful.
So first, we’ll be looking at the NumPy python library. Some pronounce it Numpee
while others pronounce it numpeye, but I’ll probably use both versions interchangeably.
So Numpy is a python library that provides a multidimensional array object.
Multidimensional meaning you can have arrays inside of arrays inside of arrays and things
like that. Numpy provides several built in operations for arrays such as sorting, selecting,
mathematical operations, etc. Now in theory, numpy arrays look quite similar to python lists.
But Numpy arrays are actually much more effective in terms of size, performance, and functionality.
They take up less space in the memory of the machine, they have faster computational
abilities than python lists, and they have more optimized and advanced functions.
Next, let’s take a look at Pandas. Pandas is a powerful library that allows you to
analyze data with Python. It takes data, for example in a CSV file, and creates a dataframe,
basically a python object with rows and columns that looks like a table or an excel spreadsheet.
This is much more efficient than data analysis using just Python lists/dictionaries/etc.
Now let’s talk about matplotlib. Matplotlib is a Python library for plotting data and creating
data visualizations such as line graphs, bar graphs, scatter plots, histograms, 3D plots,
pie charts, etc. Plots help us understand trends, patterns, and correlations between
the variables we are working with. And seaborn, which is the last library we’ll be exploring,
is an basically extension of matplotlib as it also helps with data visualization but has some
more advanced features (more plot options, better personalization (fonts, color, etc.), and advanced
output). Now for all four of these libraries, you’ll have to install some packages onto your
machine. The installation instructions for each of the packages is linked in the description below.
You’ll also need to download a python IDE or a workspace on which you can type and run your code.
I will be using Jupyter notebook that’s part of the Anaconda distribution for these videos,
and I highly recommend you get Anaconda and use Jupyter because it is extremely
easy to use and is highly efficient. If you already have a workspace,
then you can skip this step, but if you don’t, then you can follow the instructions
on the website linked in my description box below. Well, that’s all I have for you for
now, but I’m really excited to embark on this journey with you and I hope you are too. If
you’ve enjoyed the content in this video, make sure to give it a like and feel free to comment
any questions you may have in the comment section down below. If you are new here, then make sure to
subscribe and turn on notifications so you don’t miss any videos that’ll help you on your journey
towards mastering artificial intelligence. Thanks for watching, and I’ll see you in the next one.