Python AI

Artificial Intelligence (AI) and Machine Learning (ML) are the new black of the IT industry. While discussions over safety of its development keep escalating, developers expand abilities and capacity of artificial intellect. Today Artificial Intelligence went far beyond science fiction idea. If you are wondering if is python good for AI you just have to know that AI has become the necessity. Being widely used for processing and analyzing huge volumes of data, AI helps to handle the work that cannot be done manually anymore because of its significantly  increased volumes and intensity.

For instance, AI is applied in analytics to build predictions that can help people create strong strategies and look for more effective solutions. FinTech applies AI in investment platforms to do market research and predict where to invest funds for bigger profits. The traveling industry uses AI to deliver personalized suggestions or launch chatbots, plus enhance the overall user experience. These examples show that AI and ML are used  process loads of data to offer better user experinece, more personal and accurate one.

In this article we will show you why Python is used in artificial intelligence and machine learning and why Python is good for machine learning and AI.

Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.

 

AI has created a world of opportunities for application developers. AI allows Spotify to recommend artists and songs to users, or Netflix to know what shows you’ll want to see next. It is also used extensively by companies in customer service to drive self-service, and improve workflows and employee productivity.

But what is it about Python that makes it so good for AI? In this article, we’ll take a look at the main reasons why Python is the go-to programming language for developers working in the fields of machine learning and deep learning and why you should consider it for your next AI project.

Machine Learning is the process of teaching computers to make predictions based on some data without the interference of human beings. Currently the best programming language for research and development in Artificial Intelligence (AI) and Machine learning. We have an example of Google research about language ranking. Which told us that the interest in Python language has reached the next level in recent five years when compared to other programming languages like Java, R, Scala, Julia, and spark. Now the question arises is that Why, Why Python has become the most popular language for AI and Machine Learning. Let’s have a look at four basic reasons for its noteworthy trend mark.

Huge number of libraries and frameworks

Building AI/ML applications is complex and time-consuming. However, there are many libraries that are compatible with Python. This is the primary reason developers prefer it over other languages.

Note: Libraries are pre-written pieces of code that can be reused by importing them in the editor and calling the function.

For example, the Scikit-learn library provides a number of implementations of ML algorithms like linear regression, logistic regression, support vector machines, etc. Other libraries include spaCy, Natural Language Toolkit (NLTK), etc. Meanwhile, TensorFlow, PyTorch, and Keras libraries are popular among the AI community. Several others like NumPy, Pandas, and Seaborn allow easy manipulation of data. These libraries, which are only available in Python, reduce the time and complexity of writing code.

Easy syntax and resembles the English language

Python’s syntax is simple and resembles everyday English. This reduces the time developers take to learn and understand the syntax and implement it. Moreover, Python doesn’t involve the use of brackets as it uses indentation, which again reduces complexity.

Python for AI and ML: The Best Programming Language

As AI and ML are being applied across various channels and industries, big corporations invest in these fields, and the demand for experts in ML and AI grows accordingly. Jean Francois Puget, from IBM’s machine learning department, expressed his opinion that Python is the most popular language for AI and ML and based it on a trend search results on indeed.com.

According to the graph from Francois Puget, Python programming for AI and ML is the major code language.

We have conducted some research on Python’s strong sides and found out why you should opt in for Python when bringing your AI and ML projects to life. So, let’s check some benefits of using Python for artificial intelligence and machine learning.

1. A great library ecosystem

A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI. A library is a module or a group of modules published by different sources like PyPi which include a pre-written piece of code that allows users to reach some functionality or perform different actions. Python libraries provide base level items so developers don’t have to code them from the very beginning every time.

ML requires continuous data processing, and Python’s libraries let you access, handle and transform data. These are some of the most widespread libraries you can use in Python for ML and AI:

• Scikit-learn for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
• Pandas for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.
• Keras for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer.
• TensorFlow for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets.
• Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization.
• NLTK for working with computational linguistics, natural language recognition, and processing.
• Scikit-image for image processing.
• PyBrain for neural networks, unsupervised and reinforcement learning.
• Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU.
• StatsModels for statistical algorithms and data exploration.

2. A low entry barrier

Working in the ML and AI industry means dealing with a bunch of data that you need to process in the most convenient and effective way. The low entry barrier allows more data scientists to quickly pick up Python and start using Python for AI development without wasting too much effort on learning the language.

Python programming language resembles the everyday English language, and that makes the process of learning easier. Its simple syntax allows you to comfortably work with complex systems, ensuring сlear relations between the system elements. That’s why using Python for AI is justified, it’s all about simplicity.

For instance, this code is written in attempt to find out if an input number is prime.

Here’s the view of the code:

test_number = 407  # our example is not a prime number
# prime numbers are greater than 1
if test_number > 1:
    # check for factors
    number_list = range(2, test_number)
    for number in number_list:
        number_of_parts = test_number / number
        print(f"{test_number} is not a prime number")
        print(f"{number} times {number_of_parts} is {test_number}")
        break
    else:
        print(f"{test_number} is a prime number")
else:
    print(f"{test_number} is not a prime number")

And as you may see in the last row, the result of this code is that the test number is not a prime one. To put it bluntly, an English-speaking person could easily understand the meaning of the code, as it uses simple English words.

In addition to this, there’s a lot of documentation available, and Python’s community is always there to help out and give advice.

3. Flexibility

Python for machine learning is a great choice, as this language is very flexible:

• It offers an option to choose either to use OOPs or scripting.
• There’s also no need to recompile the source code, Python developers can implement any changes and quickly see the results.
• Programmers can combine Python and other languages to reach their goals.

Moreover, flexibility allows developers choose the programming styles which they are fully comfortable with or even combine these styles to solve different types of problems in the most efficient way.

• The imperative style consists of commands that describe how a computer should perform these commands. With this style, you define the sequence of computations which happen like a change of the program state.
• The functional style is also called declarative because it declares what operations should be performed. It doesn’t consider the program state, compared to the imperative style, it declares statements in the form of mathematical equations.
• The object-oriented style is based on two concepts: class and object, where similar objects form classes. This style is not fully supported by Python, as it can’t fully perform encapsulation, but developers can still use this style to a finite degree.
• The procedural style is the most common among beginners, as it proceeds tasks in a step-by-step format. It’s often used for sequencing, iteration, modularization, and selection.

4. Platform independence

The next advantage of python for AI and ML development is platform independence. Python is not only comfortable to use and easy to learn but also very versatile. What we mean is that Python for machine learning development can run on any platform including Windows, MacOS, Linux, Unix, and twenty-one others. To transfer the process from one platform to another, developers need to implement several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform. Developers can use packages like PyInstaller to prepare their code for running on different platforms.

Again, this saves time and money for tests on various platforms and makes the overall process more simple and convenient.

5. Readability

Python is very easy to read so every Python developer can understand the code of their peers and change, copy or share it. There’s no confusion, errors or conflicting paradigms, and this leads to more a efficient exchange of algorithms, ideas, and tools between AI and ML professionals.

6. Good visualization options

We’ve already mentioned that Python offers a variety of libraries, and some of them are great visualization tools. However, for AI developers, it’s important to highlight that in artificial intelligence, deep learning, and machine learning, it’s vital to be able to represent data in a human-readable format.

Libraries like Matplotlib allow data scientists to build charts, histograms, and plots for better data comprehension, effective presentation, and visualization. Different application programming interfaces also simplify the visualization process and make it easier to create clear reports.

7. Community support

It’s always very helpful when there’s strong community support built around the programming language. Python is an open-source language which means that there’s a bunch of resources open for programmers starting from beginners and ending with pros.

A lot of Python documentation is available online as well as in Python communities and forums, where programmers and machine learning developers discuss errors, solve problems, and help each other out.

Python programming language is absolutely free as is the variety of useful libraries and tools.

8. Growing popularity

As a result of the advantages discussed above, Python is becoming more and more popular among data scientists. According to StackOverflow, the popularity of Python is predicted to grow until 2020, at least.

This means it’s easier to search for developers and replace team players if required. Also, the cost of their work may be not as high as when using a less popular programming language.

No need to recompile source code

Developers don’t need to recompile source code each and every time. Rather, they can quickly make changes and see the results. This flexibility is one of the biggest advantages of using Python.

Platform-independent

The Python code is capable of running on different platforms like Windows, Mac, UNIX, and Linux.

Great community support

Being a popular open-source programming language, Python has a large global community. The user base is eager to help and welcomes participation of all levels. This is also helpful when debugging code.

Readability

Python provides good readability of code since it uses indentation which properly structures the code.

Philosophy of Python

The core philosophy of Python is summarized in the document The Zen of Python by Tim Peters as stated below:

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.
• Readability counts.
• Special cases aren’t special enough to break the rules.
• Although practicality beats purity.
• Errors should never pass silently.
• Unless explicitly silenced.
• In the face of ambiguity, refuse the temptation to guess.
• There should be one – and preferably only one – obvious way to do it.
• Although that way may not be obvious at first unless you’re Dutch.
• Now is better than never.
• Although never is often better than right now.
• If the implementation is hard to explain, it’s a bad idea.
• If the implementation is easy to explain, it may be a good idea.
• Namespaces are one honking great idea – let’s do more of those!

The Role of Programming Languages in AI and ML

The process of choosing a programming language for AI and ML is a significant moment since the success of the future project will depend on it. Before making the final decision it’s recommended to consider certain things. For example, efficiency, library support, and flexibility.

1. Efficiency
   We measure efficiency by the ability of a certain language to execute commands swiftly and optimally handle CPU and memory usage. An efficient language promotes faster development and deployment of AI and ML applications, which is vital in industries where rapid solution delivery can make a big difference.
2. Library support
   Libraries are collections of pre-written code. In general, developers use libraries to save time and avoid repetition. They are particularly critical in AI and ML, where specific libraries can significantly accelerate the development process by providing ready-to-use algorithms and data structures. For AI and ML projects, development languages with a comprehensive range of robust libraries are preferred.
3. Flexibility
   A flexible language is versatile and adaptable. Flexible languages also are capable of working in different environments and meeting various requirements. Basically, developers can mold the language to their needs, rather than adjusting the product to the language. In the active field of AI and ML, the flexibility of the programming language can be a major factor.

There are many popular AI programming languages such as Python, Java, Julia, Haskell, and Lisp. A suitable AI programming language should be easy to learn, read, and deploy. The language will determine how easily you can program AI applications, which and how many libraries, tools, and frameworks are available, how accessible is the documentation, and how active the community is to provide support if needed.

In general, Python is used in Artificial Intelligence and Machine Learning by most programmers. Other top competitors include Java, C++, and JavaScript — but Python is considered to be the best programming language for AI.

Top Reasons Why Python is the Best Choice For AI and ML

Python is the preferred choice by most of the AI developers. But why is Python so widely used in Artificial Intelligence and Machine Learning? And how exactly is Python used in AI and ML development? The answer lies in the top benefits of this programming language that have nothing to do with AI specifically.

• Simplicity and readability are the key reasons coders use Python for AI and Machine Learning. Python is designed to be easy to understand and write. It helps developers concentrate on the problem-solving aspects of AI and ML.
• Python allows you to run the script on GPU that can be comparatively faster than CPU. Enterprises generally prefer GPUs because most AI applications require parallel processing of multiple calculations, for example, neural networks accelerated AI, and deep learning operations with massive parallel inputs of data.
• Libraries. Another advantage of Python for AI and ML is the diversity of Python standard libraries that cover a lot of ground, reducing the need to code everything from scratch. Libraries such as NumPy, Pandas, Tensorflow, Pytorch and Matplotlib take care of the numerical aspects and data visualization, while SciPy brings in additional scientific computing capabilities.
• Interoperability is another advantage of Python. It leads to easy communication with other languages like C and C++, enabling it to leverage optimized code pieces for computationally intensive tasks. This results in a better performance. Moreover, Python’s extensive community support ensures that developers can quickly find solutions and get help when needed.

Why is Python the Optimal Choice for AI Development?

1. Rich Ecosystem of Libraries and Frameworks

As mentioned above, Python’s diversification of libraries is the key reason why Python is used for AI. A library is a module that is published by various sources, for example, PyPi which includes a pre-written piece of code. All of this allows users to reach some functionality or perform different actions. Python libraries provide base-level items so developers don’t have to code them from the very beginning every time. Regarding ML, it requires continuous data processing, but Python’s libraries let you access, handle, and transform data.

Python Frameworks and Libraries for AI and ML

Libraries	Usage
Scikit-learn	Is the best for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
Scikit-image	Is used for image processing.
Pandas	Is applicable for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.
Keras	Is the best for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
TensorFlow	It is suitable for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets.
Matplotlib	Is applied for creating 2D plots, histograms, charts, and other forms of visualization.
NLTK	Coders are using it for working with computational linguistics, natural language recognition, and processing. Scikit-image works for image processing.
PyBrain	For neural networks, unsupervised, and reinforcement learning.
PyTorch	It is used for applications such as computer vision and natural language processing.

2. Simple and Readable Syntax

Many coders agree that Python is more intuitive than other programming languages, which makes Python more user-friendly to work with and easy to learn. Developers can easily understand Python code, as well as understand the basics of Machine Learning. Moreover, since Python is a general-purpose language, it can do a set of complex ML tasks and enable building prototypes quickly, and testing the results of your work.

3. Strong Community Support

This is a very important factor because developers can access all the necessary educational information or materials easily. Additionally, Python is an open-source language, and a lot of Python documentation is available online as well as in AI communities and forums, where programmers and Machine Learning developers discuss errors, solve problems, and help each other out. The Python programming language is free as is the variety of useful libraries and tools.

4. Platform Independence

Why is Python an independent platform?

• It works on any platform (Linux, Windows, macOS)
• Python code can be used to create standalone executable programs
• Python software can be easily distributed and used on those operating systems without a Python interpreter

5. Popularity

The growing popularity of Python is predicted and expected. Its ability to integrate with emerging technologies, the dominance of Data Science and Machine Learning, and its advantages for web development and automation make Python a better choice compared to other programming languages.

When checking public repositories on GitHub it becomes evident that Python for ML is mentioned way more compared to other languages. Let’s review the data.

Language	Public repositories
Python	34,766 times
JavaScript	2,998 times
R	2,105 times
C++	1,926 times
MATLAB	1,881 times
Java	1,783 times
C#	808 times

6. Data Analysis and Visualization

We’ve already mentioned that Python offers a variety of libraries and some of them are great visualization tools. It’s significant for AI coders to be able to represent data in a user-friendly format. So, libraries like Matplotlib allow data scientists to build charts, histograms, and plots for better data comprehension, effective presentation, and visualization.

7. Cross-Domain Versatility

Python can be used for multiple tasks such as: devising machines, learning algorithms, automation of machines, or just data analysis. In addition, Python is an excellent choice for web development, data science and analytics, AI/ML projects, etc.

Python Use Cases for AI and ML

Python grants many features helpful for AI and ML in particular, and this makes it the best programming language for AI. Therefore, it’s apparent why various industries and businesses use Python for predictions and Machine Learning tasks.

Industry	Usage
Travel	AI assistants for booking (Skyscanner, Booking.com), AI-driven applications for flight forecasting (Hopper), room mapping + dynamic price tracking (Makcorps Hotel Pricing).
FinTech	Fraud detection and prevention (Stripe Radar), automated credit scoring (Credit Karma), personal finance management (Wally, Digit), and risk management (inscribe.ai).
Transportation	Autonomous vehicles (Tesla, BMW, Waymo, Audi), flight delay predictions, and real-time vehicle tracking.
Healthcare	Medical imaging (CT, MRI), electronic health records, early diagnosis of fatal blood diseases, and management of medical records, patient care apps (Aicure.com)
Education	Personalized language learning (Duolingo Max), ChatGPT-powered roleplay for soft skills development (Virtual Speech), interactive learning environments (Khan Academy).

Other AI programming languages

As it was described, Python is among the most popular programming languages for AI development due to its versatile ecosystem of libraries. But other programming languages like Java, C++, and Haskell also play a fundamental role in creating AI systems.

Programming language	Benefits for AI/ML Projects
Java	Various in-built modules and libraries like JUnit and Apache Commons save development time and facilitate the creation of high-performance applications. For instance, Java is commonly used in developing simulations, robotics, and embedded AI applications. A Java extension like RTSJ allows developers to create real-time systems like bots. Java also supports multithreading, which is crucial for AI tasks that need to run concurrently.
JavaScript	It’s primarily a client-side scripting language that runs directly on users’ browsers. As a result, it can help perform various computation tasks and improve the performance of AI applications. It’s also valuable when implementing real-time AI applications like virtual assistants, chatbots, and recommendation engines.
Julia	Julia is popular in the AI community, primarily because of its high performance and rapid prototyping capabilities. Julia offers fast processing abilities, which are essential in AI development because of the vast amounts of data involved.
Haskell	Haskell operates on a functional programming paradigm with core principles like pattern matching, high-order functions, and immutability, which are conducive to processing and manipulating complex data structures in AI development.
C++	While C++ can interact with Machine Learning libraries like TensorFlow and PyTorch through bindings or APIs, these libraries are primarily designed for use with Python. However, the ability to use these libraries from C++ allows developers to integrate advanced functionalities into their applications rather than create everything from scratch. C++ supports parallel computing and has multithreading abilities.
R	R has a wide range of data visualization libraries, such as Ggplot2, Lattice, and Shiny, which allow developers to quickly create graphs, charts, and other visuals with their data. Its in-built functions, like glm and lm, allow professionals to use statistical modeling.