Our Expert’s Gradual Guide To Use Artificial Intelligence With Python in 2024

Python is an open-source language that has gained a lot of popularity in recent times because of its advancements in fields of artificial intelligence and image processing. Today we will talk about using Python With Artificial Intelligence.

It is simple to learn, and it is a high-level language. Various packages are available in Python, using which you can easily perform complex tasks such as data gathering, data preprocessing, data cleaning, applying AI-based algorithms, etc.

In this article, our Adept Developers have shared their thorough knowledge on the concept to clear all your doubts about using AI with Python. We are going to discuss various libraries available in python. Also going to debunk the common myth that AI (Artificial intelligence) and ML (Machine Learning), and DL (Deep learning) are all the same.

We will be discussing various learning methods and the algorithms typically implemented in a certain model of learning. You can hire python developers to develop such solutions.

Basic Concept Of AI

The concept of Artificial Intelligence is very simple – the computers should be able to do what humans are doing. And it does that by learning.

We give a lot of data to AI systems to make it understand how to make decisions. Now when next time, the system gets similar data, based on previous learning it will give you answers.

Let me clear this with a very simple example. When you were born, were you able to classify between apple and orange? Probably, not. Only after you learned in school or your parents taught you, you were able to classify it.

The Need for Learning AI

In this ever-changing world of technology, AI has now become a necessity to learn to excel in your life. There are various reasons for this:

AI can read a lot of data and analyze it

With the amount of data we are dealing with currently, it is impossible to manually derive relevant information from it.  This is where AI helps to get relevant data as well as insights from that data.

The amount of data is increasing

And even if you are able to get insights from the data you have currently, remember the data will keep on increasing, and at one point, it would become impossible for you to analyze it.

You can achieve accurate results

The accuracy of AI-based algorithms can be increased with more data. With continuously evolving data, you would get better results with AI rather than doing it manually.

AI can help you organize your data

The most tedious task is to classify the data. Don’t worry, AI can help you here. Once you give the system a labeled data, it will learn and start classifying the data next time it gets it.

AI can learn by itself

There are instances when the data keeps changing and normal algorithms don’t work. But with recent advancements in the field of AI, scientists are able to come up with models that can learn important characteristics on their own.

Real-time response

Humans can take a lot of time to collect data and derive insights. But once you have trained the AI model, it can help you get insights into your data in real-time and enable you to make quicker decisions. So, Artificial Intelligence with Python can be great.

What’s Involved in AI?

AI is a very vast field and there are many subfields involved in it. We will discuss the most important fields present in it.

Machine Learning

It is one of the most relevant fields among all other presents in the field of AI. Most of the research since 1980 has revolved around machine learning.

In machine learning, we identify important parameters or features that are present in data and try to apply algorithms on top of those features.

Let’s take a simple example. You are given a data set of human beings with fields as age, name, address, education, and other relevant information. And you have to find their expected earnings.

So, you might relate that name is of least importance and education is an important factor. So you handpick features and apply algorithms like SVM and linear regression to train the model.

Logic Programming

In the field of logic programming, scientists use methods to enable machines to do reasoning based on the data.

In this, logic is represented by the knowledge and it is manipulated using inference. It is used in segment analysis and code analysis. So, Artificial Intelligence Python is awesome.

Fuzzy Logic

The term fuzzy logic was coined in 1965 by Lotfi Zadeh. He introduced a new theory called the fuzzy set theory.

It uses a concept that people don’t take decisions based on precise and exact numerical information that is available.

This vagueness is represented using mathematical models in fuzzy logic. This allows you to build models that can support this vagueness and let you represent, manipulate, and interpret model data.

Artificial Neural Networks

Since 2010, neural networks have gained a lot of popularity and is an emerging promising new field in AI. We just saw in machine learning how to identify the important features.

The neural network takes this one step ahead. It identifies important features by itself and applies algorithms on top of that.

Natural Language Processing

Natural language processing is a field that deals with text, speech, and language. It builds models based on the above input. So, Artificial Intelligence with Python can be awesome.

Recently Google has launched a very good NLP based algorithm called BERT which identifies various speech patterns. It was used to build an application, which was able to generate songs on its own.

Reinforcement Learning

It is a mix of supervised and unsupervised learning. In Reinforcement Learning, initially, you provide it with a list of data that are labeled.

You will also provide data, which doesn’t have labels. The model will then analyze both the data and then derive important features from it and build the model.

Why Python For AI?

Currently, Python is the best-suited language for building AI-based algorithms. In Fact, Python With Artificial Intelligence is a great combo. Below are the reasons for this:

Less Code

Python has a wide range of libraries that are available directly. You just need to add code to prepare data and provide it to the model.

All the model’s algorithms are already coded in packages. This reduces the code size drastically and you can concentrate on data rather than debugging model code.

Prebuilt Libraries

All the major algorithms that are currently used in the market are available as a prebuilt python library. You just need to add these libraries and you are all set for deriving insights from it.

Rapid Experimentation

All the new algorithms that are becoming famous come along with a prebuilt python library which has the algorithm code. This allows you to experiment rapidly with newer algorithms that are coming on the market.

Ease of Learning

All the python libraries designed for AI have a similar interface. The way they clean data, apply algorithms, and provide you with insights are almost similar in all the libraries. This allows you to learn the code quickly and become productive.

Platform Independent

Python language is platform agnostic. You can run the same code on Windows, Linux, or Mac. This allows you to port your code easily among different platforms.

Community Support

Python has a very big community and it is very supportive. You can quickly get answers to all the questions you have. You also get code samples.

There are various blogs also present on various social media platforms which discuss in detail implementation for various algorithms also.

Type of Artificial Intelligence

There are mainly three types of artificial intelligence:

Artificial Narrow Intelligence

Artificial narrow intelligence or also known as Weak AI focuses on one specific topic. It takes one problem and works on it. For instance, it tries to solve a chess game or analyze the raw data to determine specific insight.

It operates with a specific mindset. It does not consider human emotion or tries to mimic the exact step which a human would do. Most of the AI algorithms that are running in production are currently narrowed AI.

Google Siri, Amazon Alexa is all based on weak AI because they execute a specific task. Though these applications are powerful, they are still a weak AI as they are not as good as humans. They lack self-awareness and consciousness and cannot replicate the human mind.

Artificial General Intelligence

Artificial general intelligence, or also called as Strong AI, is as smart as human beings. They can perform all the actions that any human being can do. For instance, as shown in sci-fi movies like “Her” or web-series like “Upload”.

We have not yet been able to achieve this kind of awareness like artificial general intelligence but hopefully, in the near future, we should be able to do it.

As humans, we can think and strategize and take decisions, but machines are not able to think like humans. Artificial Intelligence Programming with Python is a possibility.

Artificial Super Intelligence

An Oxford professor defines artificial super intelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”. The computers would be able to perform much better than any human being.

This is particularly raising fears among the people and they feel they would not be able to control the computers once the computers become more smart and intelligent. So, Artificial Intelligence with Python can be massive.

Artificial Intelligence vs Machine Learning vs Deep Learning

The biggest misconception present in the beginners is all AI/ ML and DL are the same. But in truth, all three are completely different. Let’s delve into more detail for each of the techniques.

The above image will clarify all the misconceptions:

• Machine learning is a subset of Artificial intelligence. It is one of the fields in the vast field of AI.
• Deep learning is a specific specialization in the field of Machine learning.
• Artificial intelligence has a lot of sub-specialization like NLP, Logic Programming, or expert systems.

Basically, all three are interconnected fields. Machine Learning and Deep Learning provides you with specific insights into data and allows you to make data-driven decisions by giving you algorithms. That’s why Artificial Intelligence Python is a great combination.

Python Libraries for Artificial Intelligence

We would try to list important python libraries that you can use to build a model. This can help you in using Python With Artificial Intelligence.

General AI

• AIMA-Python : This library implements the algorithm from Russell and Norvig’s Artificial Intelligence: A Modern Approach.
• PyDatalog : PyDatalog is a particularly useful library to implement logic programming.
• Simple : This library again provides you with the implementation of various algorithms from Artificial Intelligence: A Modern Approach. It has a very simple interface, and it is very easy to use
• Easy : It is designed particularly for two-player games like tic tac toe, connect4, reversi.

Machine Learning

• Scikit-learn : This is by far the most used machine learning library. It provides you regression as well as classification algorithms. Most online courses are taught using Scikit-learn.
• GraphLab Create : It is built with python as front-end and C++ as the core. You can easily build as well as deploy your model in production using this library.
• Orange : Orange is a library that provides you with a data visualization tool. Always remember, machine learning is just not about accuracy, you should also be able to get insights by visualizing the output. At this time Orange is of great help.
• PyBrain : It is again a famous library developed with the aim to provide you with a modular way of adding algorithms and building models.
• PyML : It is an object-oriented based library designed specifically for the support vector machine (SVM) algorithm. Though, it runs only on Linux OS and Mac OS

Natural Language & Text Processing

• NLTK : Like Scikit-learn is the most used library for machine learning, the NLTK library is mostly used for natural language processing.
• Gensim : Gensim is a python-based framework written specifically for doing sentiment analysis. The main use case of sentiment analysis is for analyzing user feedback. Let’s say you have an application on the app store and users give feedback.
• Query : It is a very interesting NLP based library that converts the queries written in natural language to the actual database query.

Neural Networks

• TensorFlow : It is developed by Google and has gained a lot of popularity among deep learning experts. All deep learning courses are taught either using TensorFlow or Keras.
• Keras : Keras is again a popular open-source library that provides you with all the functionality related to deep learning and neural nets.
• Neurolab : It is a simple library that provides the basic functionality of neural nets. Neurolab uses pure python and numpy libraries to build algorithms.

Harikrishna Kundariya

CEO, eSparkBiz

Harikrishna Kundariya, a marketer, developer, IoT, chatbot and blockchain savvy, designer, co-founder, Director of eSparkBiz @Software Development Company where you can Hire Software Developers. His 14+ experience enables him to provide digital solutions to new start-ups based on Web app development.