Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Developing Models
You will need to get yourself set up in Python or some other programming
language to do machine learning. You create machine learning models by
using code to manipulate the datasets. While this book doesn’t cover coding
for machine learning, I will give you a quick rundown of some basic
libraries and packages that I recommend you install for machine learning.
Because it’s the most common language used in data science, we will use
Python as an example throughout this chapter. I also think it’s the most
practical language to learn if it’s your first language because it’s more
readable than other programming languages, and it has a wide range of
abilities beyond machine learning.

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Once you’ve installed the latest version of Python, there are a few
recommended libraries to install which come with a lot of commands that
will be useful to your work with machine learning. All of these can be
found easily with a quick google search, and they can be downloaded for
free.
The most important library for data analysis and machine learning in
Python is called Pandas. It’s quite a popular choice for datasets and will
make your coding easier and faster, especially when you are still trying to
get a feel for things.
Anaconda for python
Another option for getting yourself started with Python is installing
Anaconda. The great thing about Anaconda is that it gives you every
package for Python so that you don’t have to install packages one at a time
while you write the program for your model. It comes with all the libraries
you will need, for just about every different kind of function.
Anaconda is a free and open-source program that will work in both R and
Python. With Anaconda, you'll have access to several libraries that will help
you with your data science projects. Basically, this gives you a pre-
packaged collection of all the python libraries, of which there are over 100
libraries.
One of the major libraries is Spyder and Jupyter. Both of these are
integrated development environments, meaning they are the window where
you will write your code, but they are more developed than a standard
command window and have options to save and export/import codes.

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Most Python users will start in a development environment called IDLE.
It’s very simple and offers a good format for learning how to code in
python. When you install Python on a windows computer, it will come
automatically included. If you have a Linux computer, it is available, but
you will need to install it separately.
IDLE will make those baby steps in Python easier because you’ll be able to
save your scripts and edit them later. It will also walk you through
debugging.
To install Anaconda, visit:
docs.anaconda.com/anaconda/install
Scroll down until you see a list of operating systems. Choose your operating
system. It will give you instructions for installing anaconda on their
website, based on your operating system. Then you're ready to start messing
around in Python. I highly recommend using one of the free beginner
Python tutorials that are available on the internet. EdX has a free beginner
tutorial in Python, which is a great place to start. Also, take advantage of
forums like Reddit, where there have been a vast number of common
questions already answered in detail, and members are always sharing
relevant news from the world of machine learning.