Artificial intelligence (AI) is a new side of the IT industry. While discussions about the security of its development are constantly escalating, developers are expanding its capabilities and capacities. Today, artificial intelligence has far surpassed the idea of science fiction. It became a necessity. Because it is widely used to process and analyze vast amounts of data, AI helps handle jobs that can no longer be done manually due to significantly increased volume and intensity.
Projects based on artificial intelligence, along with machine learning (ML), are obviously what the future holds. We want better personalization, smarter referrals, and improved search functionality. Our applications can see, hear, and respond – that is what artificial intelligence has brought, improving the user experience and creating value in many industries.
Python can additionally be successfully applied to the marketing strategy in a way that will allow you to automate all of your marketing activities:
- It may be used to build useful and professional-looking visualizations, as well as to improve your marketing analyses.
- Python can let you collect data from numerous sources more efficiently.
- Python can help you get more out of your data analytics.
- Customer segmentation, feedback analysis, A/B testing (such testing is especially important in the e-mail marketing strategy when employing tools for email automation, e.g. VerticalResponse, that is to segment the proper audience for the newsletter), and other marketing tasks can all be automated. There is no need to look for hard-to-find marketing experts.
- Adopting detailed attribution models can help you identify which channels bring you the most customers, which will improve the accuracy of your ROI calculations.
- Allow sophisticated machine learning models to determine which ad should be shown to which customer and when, and then reap the benefits of enhanced ROI.
Now you are probably faced with two questions: How can I bring these experiences to life and what programming language is used for AI? Well, consider using Python for AI…
How Does AI Shape the Technologies of the Future?
Today, with the increasing volume and complexity of data, AI is used for its processing and analysis. The ability of the human brain to analyze enormous volumes of data is limited by the amount of data it can absorb at one time. Artificial intelligence is free from this limitation. AI’s more precise predictions and insights improve corporate efficiency, reduce production costs, and boost productivity. It is no surprise that numerous industries use AI and machine learning to boost performance and drive product development.
Given the stated advantages of using AI, more and more companies are eager to use it. However, AI is a two-way street – it is used to optimize the analytical process, but it is not the simplest technology to develop. Due to the huge amount of data that needs to be analyzed, the AI product must be able to cope efficiently with the busy process and does not need too much time for that. In order to function properly, the appropriate language for its development must be selected – one that will not be too complex in terms of syntax, that will be able to deal with sophisticated processes, and is easy to support. As AI is applied in a variety of channels and industries, large corporations are investing in these areas, and the demand for niche AI professionals is growing accordingly.
Why Can a Developer Easily Rely on the Effectiveness of Python in Accomplishing Things?
Exceptional library environment
The outstanding choice of libraries is one of the main reasons why Python is the most common programming language used for AI. A library is a module or set of modules that are distributed from various sources such as PyPi that include a pre-compiled bit of code that allows clients to access some utility or perform various activities. Python libraries provide things at a basic level, so designers don’t necessarily have to code them from the earliest starting point. AI requires continuous preparation of information, and Python libraries allow you to access information, process, and change information.
These are the absolute best libraries you can use for AI but also ML:
- Pandas for information structures and investigations at an elevated level. They allow you to combine and sift information, just as you would with other external sources such as Excel.
- Keras for deep learning. It allows fast counting and prototyping because it uses a GPU regardless of the computer’s CPU.
- TensorFlow for working with deep learning by setting up, preparing, and using artificial neural systems with large data sets.
- Matplotlib for creating 2D charts, histograms, and different types of charts and presentations.
- Natural Language Toolkit (NLTK) for working with computer etymology, universal language recognition, and handling.
- Scikit-image for image handling.
- PyBrain for neural systems, solo learning, and support.
- Caffe for a deep-detection that lets you exchange between CPU and GPU and handle 60+ million images a day using a lone NVIDIA K40 GPU.
- Statistical models for measurable calculations and information research.
Basic and predictable
Python offers short and decrypted code. Although complex calculations and flexible workflows remain behind AI, Python allows engineers to assemble robust frameworks. Designers, for example, find a feasible pace in their ML problem-solving, as opposed to concentrating on specialized language subtleties. Moreover, Python code is human-friendly, which simplifies building models for AI.
Numerous software engineers claim that Python is more intuitive than other programming dialects. Others say it brings more systems, libraries, and extensions that improve the execution of various functionalities. It is commonly recognized that Python is suitable for shared performance when a number of engineers are involved. Because Python is a widely useful language, it can do many complex AI tasks and empower you to quickly build models that allow you to test your subject for AI purposes.
Flexibility
Python for machine learning is a great choice because this language is very flexible. It offers the ability to choose whether to use OOPs or scripting. It is also not necessary to recompile the source code – developers can apply any changes and see the results quickly. Python and other languages can be used by developers to achieve their objectives. Moreover, flexibility allows developers to choose programming styles that are completely pleasing to them or even combine them to solve different types of problems in the most efficient way.
The imperative style consists of commands that describe how the computer should execute these commands. This style defines the order of calculations that occur as a change in program state.
The functional style is also called declarative because it declares which operations should be performed. It does not consider the state of the program, and instead asserts statements in the form of mathematical equations, as opposed to the imperative style.
The object-oriented style is built on two concepts: class and object, with classes containing related items. Although Python does not fully support this approach due to its inability to fully encapsulate, developers can nevertheless make full use of it.
Procedural style is the most common among beginners because it performs tasks in a step-by-step format. It is often used for sequencing, repetition, modularization, and selection. The flexibility factor reduces the possibility of mistakes because developers have the opportunity to take control of the situation and work in a pleasant environment.
Platform independence
Python is not only comfortable to use and easy to learn but also versatile. Python for AI development can run on any platform, including Windows, macOS, Linux, Unik, and 21 others.
To move the process from one platform to another, developers need to apply several small-scale changes and modify some lines of code to create executable code for the selected platform. Developers can use packages like PyInstaller to prepare their code to run on different platforms. Again, this saves time and money on tests on different platforms and makes the whole process simpler and more convenient.
Readability
Python is very easy to read, so any Python developer can understand the code of their peers and modify, copy, or share it. There is no confusion, errors, or conflicting paradigms, and this leads to a more efficient exchange of algorithms, ideas, and tools between intelligence professionals and AI (and ML).
Tools like IPython – which is an interactive shell that provides additional features like testing, debugging, card completion, and more, and makes the workflow easier – are also available.
Good visualization options
Python has a wide range of libraries, some of which are excellent visualization tools, as we’ve already said. However, it is critical for intelligence developers to underline that the capacity to convey data in an understandable style is critical in artificial intelligence, deep learning, and machine learning.
Libraries like Matplotlib allow data scientists to create tables, histograms, and charts for better data understanding, efficient presentation, and visualization. Different application programming interfaces also make it easier to develop concise reports and ease the visualization process.
Community support
When there is a strong community established around a programming language, it is always beneficial. Python is an open-source language, which means that a wealth of materials is available to developers of all levels, from beginners to experts.
There is a wealth of Python material online, as well as in Python communities and forums where developers discuss bugs, solve problems, and assist one another. The Python programming language, as well as a number of important libraries and tools, is completely free.
Growing popularity
As a result of the above advantages, Python is becoming increasingly popular among data scientists. Python’s popularity was predicted to grow at least until 2020 or this year.
This means that it is easier to look for developers and replace ‘team players’ if necessary. Also, the cost of their work may not be as high as when using a less popular programming language.
Conclusion
Python is one of the most popular programming languages, unrelated to AI specifically. This is also shown by reports based on how many times some of the tutorials for a particular programming language have been searched on Google. While this does not necessarily mean that Python is also the most widely used programming language, it does show that it is very popular and that users most often want to learn this language. At this time last year, the percentage of users who searched for Python tutorials was as high as 31 %, with a growth index compared to 2019 of almost 4 %. It is followed by Java with some 17 % and JavaScript with almost 8 %. However, they have a slight decline in popularity compared to 2019.
In addition, the growth of Python’s popularity over the past 5 years is as much as 20 %, while Java for the same period recorded a decline of about 8 %.
