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The Complete Guide To Artificial Intelligence With Python

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Artificial intelligence has been around for almost a half-century, and its developments are accelerating. The need for AI is at an all-time high, and if you want to learn more about it, you’ve come to the right place. This blog on Artificial Intelligence With Python will assist you in understanding all of the basics of AI through actual Python applications.

The ability of computers or machines to accomplish various activities has grown at an exponential rate since their introduction. Humans have increased the power of computer systems in terms of their broad working areas, growing speed, and shrinking size over time.

Artificial Intelligence with python is an area of computer science that aims to create computers or machines that are as intelligent as humans.

Why Python for AI?

Python with Ai

Artificial intelligence is the newest breakthrough in technology. It has already been used to create several apps. As a result, it has garnered interest from many businesses and academics. Which programming language may be utilized to develop these AI applications, however, is the essential issue at hand. A number of programming languages, including Lisp, Prolog, C++, Java, and Python, can be used to create AI applications.

Simple syntax & less coding

Python, unlike other programming languages that may be used to construct AI applications, requires relatively little code and has a straightforward syntax. This feature makes testing easy and allows us to focus more on development.

Inbuilt libraries for AI projects

One significant advantage of adopting Python for AI is that it has built-in libraries. Python includes libraries for nearly every type of AI project. NumPy, SciPy, matplotlib, nltk, and SimpleAI are some of Python’s significant inbuilt libraries.

  • Open source:-Python is a free and open-source programming language. As a result, it is immensely popular in the community.
  • Can be used for a broad range of programming:- Python may be used for a wide range of programming jobs, from tiny shell scripts to business web applications. This is just another reason Python is appropriate for AI applications.

Features of Python

Features of python

Python is a scripting language that is high-level, interpreted, interactive, and object-oriented. Python is intended to be extremely readable. It commonly employs English terms rather than punctuation, and it has fewer syntactical structures than other languages. Python has the following features:

  • Easy-to-learn:- Simple to learn Python has a small number of keywords, a basic structure, and a well-defined syntax. This enables the pupil to swiftly learn the language.
  • Easy-to-read:-Simple to read Python code is more defined and visible to the naked eye.
  • Easy-to-maintain:-Python’s source code is relatively simple to maintain.
  • A broad standard library:-Python’s majority of the library is relatively portable and cross-platform compatible on UNIX, Windows, and Macintosh.
  • Interactive Mode:-Python has support for an interactive mode, which enables interactive testing and debugging of code snippets.
  • Portable:-Python is portable because it can operate on a broad range of hardware systems and has the same interface across all platforms.
  • Extendable:-We can extend the Python interpreter by adding low-level modules. These modules allow programmers to enhance or adapt their tools to make them more efficient.
  • Databases:- Python provides interfaces to all major commercial databases.

Installing Python

python installation

Python distributions are available for a wide variety of systems. You simply need to download the binary code for your platform and install Python.

If your platform’s binary code isn’t accessible, you’ll need a C compiler to manually generate the source code. Compiling the source code gives you greater options in terms of the features you want in your installation.

Here’s a quick overview of how to install Python on various platforms.

Unix and Linux Installation

unix & linux python installation

To install Python on a Unix/Linux system, follow these instructions.

  • Navigate to https://www.python.org/downloads in your browser.
  • Follow the link to get the zipped source code for Unix/Linux.
  • Download and unzip the files.
  • If you wish to change certain settings, edit the Modules/Setup file.
  • execute the./configure script.
  • make
  • make installation

This installs Python at /usr/local/bin and its libraries at /usr/local/lib/pythonXX, where XX is the Python version.

Windows Installation

Installing Python on windows

To install Python on a Windows PC, follow these instructions:

  • Navigate to https://www.python.org/downloads in your browser.
  • Follow the link to get the python-XYZ.msi Windows installer, where XYZ is the version you need to install.
  • The Windows system must support Microsoft Installer 2.0 in order to utilize this installer python-XYZ.msi. Save the installation file to your local system and execute it to see whether your machine supports MSI.
  • Execute the downloaded file. This launches the Python installation process, which is quite simple to use. Accept the default options and wait for the installation to complete.

Macintosh Installation

python for MacOs

It is advised that you use Homebrew to install Python 3 on Mac OS X. It is a fantastic package installer for Mac OS X that is really simple to use. If you don’t already have Homebrew, use the following commands to get it:

$ ruby -e "$(curl -fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)"

We can update the package manager with the command below −

$ brew update

Now run the following command to install Python3 on your system-

$ brew install python3

Running Python

Let us now see the different ways to run Python. The ways are described below −

Interactive Interpreter

Python may be started from Unix, DOS, or any other system that has a command-line interpreter or shell window.

  • At the command prompt, type python.
  • Begin coding immediately in the interactive interpreter.
$python # Unix/Linux

or

python% # Unix/Linux

or

C:> python # Windows/DOS

Script from the Command-line

A Python script can be executed at the command line by invoking the interpreter on your application, as in the following:

$python script.py # Unix/Linux

or,

python% script.py # Unix/Linux

or,

C:> python script.py # Windows/DOS

Note − Be sure the file permission mode allows execution.

Integrated Development Environment

You can run Python from a Graphical User Interface (GUI) environment as well

Unix:-IDLE was the first Python IDE on Unix.

Windows:-Python for Windows Win is the first Windows interface for Python and is an IDE with a graphical user interface.

Macintosh:- The Macintosh version of Python, as well as the IDLE IDE, may be downloaded from the main website as either MacBinary or BinHex’d files.

If you are unable to correctly configure the environment, you can seek assistance from your system administrator. Ascertain that the Python environment is correctly configured and operational.
We may also utilize Anaconda, another Python platform. Hundreds of prominent data science packages are included, as well as the conda package and virtual environment manager for Windows, Linux, and macOS.

AI with Python – Data Preparation

Data preparation

We have already investigated both supervised and unsupervised machine learning techniques. To begin the training process, these algorithms require structured data. We must prepare or format data in a specific way so that it may be fed into ML algorithms.

This chapter is about preparing data for machine learning algorithms.

Preprocessing the Data

We deal with a lot of data in our everyday lives, yet it is in raw form. We must turn the data into useful data in order to feed it into machine learning algorithms. This is where data preparation comes in. In other words, before submitting data to machine learning algorithms, we must first preprocess the data.

Data preprocessing steps

Follow these steps to preprocess the data in Python:-

Step 1 − Importing the useful packages: If we are using Python then this would be the first step for converting the data into a certain format, i.e., preprocessing. It can be done as follows −

import numpy as np
import sklearn.preprocessing

We utilized the two packages listed below.

NumPy:- is a general-purpose array-processing program designed to effectively operate large multidimensional arrays of arbitrary records while compromising performance for small multi-dimensional arrays.

Sklearn.preprocessing:-This package contains numerous popular utility methods and transformer classes for transforming raw feature vectors into a more acceptable format for machine learning algorithms.

Step 2 − Defining sample data:-After importing the packages, we must establish some example data on which to use preprocessing techniques. We will now define the example data shown below.

input_data = np.array([2.1, -1.9, 5.5],
                      [-1.5, 2.4, 3.5],
                      [0.5, -7.9, 5.6],
                      [5.9, 2.3, -5.8])

Step3 − Applying preprocessing technique:-

We must use one of the preprocessing approaches in this phase. The strategies for data pretreatment are described in the next section.

Techniques for Data Preprocessing

The techniques for data preprocessing are described below −

Binarization

When we need to turn numerical numbers into Boolean values, we apply this preprocessing procedure. We may utilize an inherent mechanism to binarize the input data, for example, by choosing 0.5 as the threshold value, as shown below.

data_binarized = preprocessing.Binarizer(threshold = 0.5).transform(input_data)
print("\nBinarized data:\n", data_binarized)

Now, after running the above code we will get the following output, all the values above 0.5(threshold value) would be converted to 1 and all the values below 0.5 would be converted to 0.

Binarized data

[[ 1. 0. 1.]
[ 0. 1. 1.]
[ 0. 0. 1.]
[ 1. 1. 0.]]

Mean Removal

It is yet another frequent preprocessing approach used in machine learning. It is used to remove the mean from a feature vector such that each feature is centered on zero. We may also eliminate the bias from the feature vector’s features. We may use the Python code below to apply the mean removal preprocessing approach to the sample data. The mean and standard deviation of the input data will be shown by the code.

print("Mean = ", input_data.mean(axis = 0))
print("Std deviation = ", input_data.std(axis = 0))

We will get the following output after running the above lines of code

Mean = [ 1.75 -1.275 2.2]
Std deviation = [ 2.71431391 4.20022321 4.69414529]

Now, the code below will remove the Mean and Standard deviation of the input data 

data_scaled = preprocessing.scale(input_data)
print("Mean =", data_scaled.mean(axis=0))
print("Std deviation =", data_scaled.std(axis = 0))

We will get the following output after running the above lines of code

Mean = [ 1.11022302e-16 0.00000000e+00 0.00000000e+00]
Std deviation = [ 1. 1. 1.]

Scaling

Scaling the feature vectors is another data preparation approach. Because the values of each feature might fluctuate between multiple random values, feature vectors must be scaled. In other words, scaling is vital since we don’t want any feature to be artificially huge or tiny. We may scale our input data, i.e., feature vectors, with the aid of the Python code below.

# Min max scaling

data_scaler_minmax = preprocessing.MinMaxScaler(feature_range=(0,1))
data_scaled_minmax = data_scaler_minmax.fit_transform(input_data)
print ("\nMin max scaled data:\n", data_scaled_minmax)

We will get the following output after running the above lines of code −

Min-max scaled data

[ [ 0.48648649  0.58252427   0.99122807]
[   0.          1.           0.81578947]
[   0.27027027  0.           1.        ]
[   1.          0. 99029126  0.        ]]

Normalization

It is one more information readiness strategy used to change the component vectors. This kind of progress is expected to quantify the element vectors on a typical scale. The two strategies for standardization that might be used in AI are as per the following.

L1 Normalization

It is also known as Least Absolute Deviations. This type of normalization adjusts the numbers so that the total of the absolute values in each row is always greater than one. It may be implemented on the input data using the Python code below.

# Normalize data
data_normalized_l1 = preprocessing.normalize(input_data, norm = 'l1')
print("\nL1 normalized data:\n", data_normalized_l1)

The above line of code generates the following output &miuns

L1 normalized data:
[[ 0.22105263 -0.2 0.57894737]
[ -0.2027027 0.32432432 0.47297297]
[ 0.03571429 -0.56428571 0.4 ]
[ 0.42142857 0.16428571 -0.41428571]]

L2 Normalization

It is also known as least squares. This type of normalization changes the data so that the total of the squares in each row is always greater than one. It may be implemented on the input data using the Python code below.

# Normalize data
data_normalized_l2 = preprocessing.normalize(input_data, norm = 'l2')
print("\nL2 normalized data:\n", data_normalized_l2)

The above line of code will generate the following output

L2 normalized data:
[[ 0.33946114 -0.30713151 0.88906489]
[ -0.33325106 0.53320169 0.7775858 ]
[ 0.05156558 -0.81473612 0.57753446]
[ 0.68706914 0.26784051 -0.6754239 ]]

Labeling the Data

We already know that machine learning algorithms require data in a specific format. Another crucial need is that the data be correctly labeled before being fed into machine learning algorithms. For example, if we consider categorization, there are several labels on the data. These labels take the shape of words, numerals, and so forth. Machine learning functions in sklearn assume that the data has number labels. As a result, if the data is in another format, it must be translated into numbers. Label encoding refers to the process of converting word labels into numerical values.

Label encoding steps

Follow these steps for encoding the data labels in Python

Step1 − Importing the useful packages

If we are using Python then this would be the first step for converting the data into a certain format, i.e., preprocessing. It can be done as follows

import numpy as np
from sklearn import preprocessing

Step 2 − Defining sample labels

Following the import of the packages, we must specify some example labels in order to develop and train the label encoder. We will now define the example labels shown below.

# Sample input labels
input_labels = ['red','black','red','green','black','yellow','white']

Step 3 − Creating & training of label encoder object

In this stage, we will design and train the label encoder. The Python code below will assist you in accomplishing this.

# Creating the label encoder
encoder = preprocessing.LabelEncoder()
encoder.fit(input_labels)

The following would be the output after running the above Python code

LabelEncoder()

Step4 − Checking the performance by encoding a randomly ordered list

By encrypting the randomly sorted list, this step may be utilized to test the performance. The Python code below may be used to accomplish the same thing.

# encoding a set of labels
test_labels = ['green','red','black']
encoded_values = encoder.transform(test_labels)
print("\nLabels =", test_labels)

The labels would get printed as follows −

Labels = ['green', 'red', 'black']

Now, we can get the list of encoded values i.e. word labels converted to numbers as follows –

print("Encoded values =", list(encoded_values))

The encoded values would get printed as follows −

Encoded values = [1, 2, 0]

Step 5 − Checking the performance by decoding a random set of numbers 

By decoding the random collection of numbers, this phase may be utilized to test the performance. The Python code below may be used to accomplish the same thing.

# decoding a set of values
encoded_values = [3,0,4,1]
decoded_list = encoder.inverse_transform(encoded_values)
print("\nEncoded values =", encoded_values)

Now, Encoded values would get printed as follows −

Encoded values = [3, 0, 4, 1]
print("\nDecoded labels =", list(decoded_list))

Now, decoded values would get printed as follows −

Decoded labels = ['white', 'black', 'yellow', 'green']

Labeled v/s Unlabeled Data

Unlabeled data mostly comprises of samples of natural or man-made objects that may be easily accessed from the outside world. They contain audio, video, photographs, news articles, and other media.

Labeled data, on the other hand, takes a set of unlabeled data and augments each item of that unlabeled data with some meaningful tag, label, or class. For example, if we have a photo, the label may be dependent on the content of the photo, such as if it is a photo of a boy, girl, animal, or anything else. Labeling data requires human skill or judgment regarding an unlabeled piece of data.

There are several circumstances in which unlabeled data is copious and easily available, while labeled data is scarce and difficult to collect.

Conclusion

Artificial intelligence with python can provide you with a wide range of options in terms of the software you use. Depending on your needs, you can choose an AI library that is specifically designed for Python, or you can use a more general-purpose AI library.

Once you have chosen your AI library, you can start working on a project using it. If you are new to AI, you may want to start by building a simple example. Once you have a basic understanding of how AI works, you can start tackling more complicated projects.

Overall, artificial intelligence with python can provide you with a powerful tool for analyzing and manipulating data. With the right library and a bit of practice, you can start building powerful AI applications of your own.

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