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Zdjęcie główne artykułu.

Why is Python the best choice for artificial intelligence and machine learning?

Artificial intelligence (AI) is a system that mimics human intelligence and thought processes to execute complex commands. An integral feature of artificial intelligence is the ability to perform machine learning. This process involves automatically improving a machine’s algorithms by processing collected data. One of the elements of machine learning is deep learning, based on the system’s own creation of artificial neural networks. The experience gathered in this way allows the system to predict events and make decisions without being programmed by a human.

"We are at the beginning of a golden age of AI. Recent advancements have already led to invention that previously lived in the realm of science fiction" ~ Jeff Bezos

Here are some examples of using artificial intelligence systems:

  • Chatbots available on websites, first to have contact with the customer - they answer questions and deal with classification of problems
  • Search engines, which select the most relevant search results based on the huge amount of data provided by internet users
  • Machine translation

Machine learning is one of the most rapidly growing computer sciences today. An artificial intelligence system, in order to develop itself, must first be properly designed. Extremely sophisticated operating algorithms are most often developed in the Python language. What distinguishes this programming language from others that makes it so suitable for designing AI systems?

Simplicity and versatility

One of Python’s many advantages is its simplicity - it can be used to write extremely complex algorithms, but its basic syntax is uncomplicated. New variables do not need to be declared, and many redundant characters, such as those ending a single line of code, have been dropped. The simple structure makes it easy for new programmers to learn the language, while forcing them to write it in such a way that it is universal and readable by outsiders.

Broad spectre of libraries

A library is a collection of ready-made functions that can be implemented in code. Packages available in the Python environment allow handling a variety of data, which is crucial in increasing the capabilities of machine learning - the more information from different sources is collected, processed and organized, the better the development of artificial intelligence. Extensive resources of libraries allow Python to create applications, applied in various industries, which provides its versatility.

Here are some examples of libraries that can be used when programming AI in Python:

  • TensorFlow - an advanced library used primarily for building deep machine learning models. Some of the capabilities of this package are image recognition or natural language processing
  • PyTorch - allows you to create and train models of self learning machines, including the performance of optimization and forecasting tasks
  • Pandas - is used to process and analyze data before it is used to train machines. Among other things, it allows you to merge data sets, handle missing information, and provides many data indexing classification functions.

Using as many ready-made functions as possible not only makes programming easier, but improves code quality and flexibility. Functions created on your own are most often not universal, and when improvements are needed, changes are usually very difficult to make. Using libraries reduces the workload and makes the code more understandable, and this allows it to be improved, even by people who were not involved in the original programming process.

Popularity and broad community

Python’s multitude of advantages, combined with open access to the software, make it extremely popular. The impressive size of the user community means easy access to instructional materials, tutorials or simply ready-made scripts, which is an invaluable aid to the programming process.

Support in almost any operating system and easy integration with other programming languages

The versatility of Python shows the fact that it is supported on all the most popular computer systems: Windows, Linux, Mac OS or Unix. Besides, algorithms can be easily adapted to many programming platforms. Similarly, it works the other way round - using Python you can easily use code fragments written in C or C++, which in some cases improves the functionality of the code.

Python is the preferred language for programming AI systems, mainly due to the combination of two features - simplicity and versatility. It can be used to write extremely complex algorithms that are required for efficient machine learning, while at the same time the basics of programming are relatively easy to learn.