Getting Started:
Installing Python 3
We will be using Python 3 throughout this
book. Make sure you have installed the latest version of Python 3 on your machine. Type
the following command on your Terminal to check:
$
python3 –version
If you see something like Python 3.x.x
(where x.x are version numbers) printed on your terminal, you are good to go. If not,
installing it is pretty straightforward.
Installing on Ubuntu
Python 3 is already installed by default
on Ubuntu 14.xx and above. If not, you can install it using the following command:
$
sudo apt-get install python3
Run the check command like we did earlier:
$ python3 –version
You should see the version number printed
on your Terminal.
Installing on Mac OS X
If you are on Mac OS X, it is recommended
that you use Homebrew to install Python 3. It is a great package installer for Mac OS X and
it is really easy to use. If you don't have
Homebrew, you can install it using the
following command:
$ ruby -e "$(curl –fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)"
Let's update the package manager:
$ brew update
Let's install Python 3:
$ brew install python3
Run the check command like we did earlier:
$ python3 --version
You should see the version number printed
on your Terminal.
Installing on Windows
If you use Windows, it is recommended that
you use a SciPy-stack compatible distribution
of Python 3. Anaconda is pretty popular and easy to use. You can find the installation
instructions at: https://www.continuum.io/downloads.
If you want to check out other SciPy-stack
compatible distributions of Python 3, you can find them at http://www.scipy.org/install.html. The good part
about these distributions is that they come with all the necessary packages pre-installed.
If you use one of these versions, you don't need to install the packages
separately.
Once you install it, run the check command
like we did earlier:
$ python3 --version
You should see the version number printed
on your Terminal.
Installing packages
During the course
of this book, we will use various packages such as NumPy, SciPy, scikitlearn, and
matplotlib. Make sure you install these packages before you proceed.
If you use Ubuntu or Mac OS X, installing
these packages is pretty straightforward. All these packages can be installed
using a one-line command on the terminal. Here are the relevant links for
installation:
scikit-learn: http://scikit-learn.org/stable/install.html
If you are on Windows, you should have
installed a SciPy-stack compatible version
of Python 3.
Loading data
In order to build a learning model, we
need data that's representative of the world. Now
that we have installed the necessary
Python packages, let's see how to use the packages to
interact with data. Go into the Python
terminal by typing the following command:
$ python3
Let's import the package containing all
the datasets:
>>> from sklearn import
datasets
Let's load the house prices dataset:
>>> house_prices =
datasets.load_boston()
Print the data:
>>> print(house_prices.data)
You will see an output like this printed
on your Terminal:
Let's check out the labels: You will see the following
printed on your terminal.
>>>>print(house_prices.target)
>>>>print(house_prices.target)
The actual array is larger, so the image
represents the first few values in that array.
There are also
image datasets available in the scikit-learn package. Each image is of shape 8×8.
Let's load it:
>>> digits =
datasets.load_digits()
Print the fifth image:
>>>
print(digits.images[4])
You will see the following on your Terminal:
As you can see, it has eight rows and
eight columns.
Summary
In this chapter, we
learned what AI is all about and why we need to study it. We discussed various
applications and branches of AI. We understood what the Turing test is and how it's
conducted. We learned how to make machines think like humans. We discussed the concept of rational agents and how they
should be designed. We learned about General Problem Solver (GPS) and how to solve a
problem using GPS. We discussed how to develop
an intelligent agent using machine
learning. We covered different types of models as well.We discussed how to install Python 3 on
various operating systems. We learned how to install the necessary packages required to
build AI applications. We discussed how to use the packages to load data that's available
in scikit-learn. In the next chapter, we will learn about supervised learning and how to build
models for classification and regression.
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