Why is Python so good for Machine Learning?

With the rise of data analysis and the increasing complexity of the web, Python has become the most widely used programming language for computer scientists. Versatile, easy to operate and, above all, compatible with any operating system, it can be used in many situations, such as software development and data analysis. Python also allows you to create programs quickly and easily, making it an excellent choice for Machine Learning.

Python: a high-level programming language

Python may still be confusing for many people, but not for data scientists. It’s anopen source programming language created by Guido van Rossum in 1991. Generally used for scripting and automating interactions with web browsers, Python is also indispensable :

  • for infrastructure management,
  • data analysis,
  • software development.

Thanks to its versatility, it has become one of the best programming languages around. Renowned schools such as explain that it is, in fact, the reference computer language. This popularity benefits both novice and expert programmers.

A programming language that’s easy to learn and use

Python is so popular because it’s so easy to learn and use. In fact, its functionalities are few and far between. Computer scientists can create programs quickly and with little effort. This ease of use was the main objective of the language when it was created. The developer behind Python wanted to make programming more accessible. For this reason, he decided to make indentation meaningful.

What’s more, Python’s syntax has been specially designed to be readable and straightforward, making it ideal for beginners. Code is therefore easier and quicker to understand: 6 to 8 weeks is all it takes to learn the basics. To make it even more accessible to novice programmers, the syntax of this programming language is similar to that of the English language. In this way, they can launch themselves into the world of development without great difficulty.

Another advantage of Python is its great flexibility. It has dynamic typing, which means that rules are not strictly defined, unlike Java. Indeed, this language doesn’t reprimand code if a variable or function has a particular name. As a result, it can operate with a certain level of error. This indulgence makes Python more intuitive and clearly more accessible.

The main uses of Python

As mentioned above, Python is used to create scripts and automate interactions with web browsers, as well as graphical application interfaces. However, his area of expertise doesn’t stop there. This language also allows you to :

  • program applications,
  • create web services or REST APIs,
  • generate code,
  • metaprogramming.

So it’s not like HTML, which is dedicated solely to web programming.

Using Python in Machine Learning

Thanks to its many libraries, which we’ll mention later, Python can also be used in Data Science and Machine Learning. In fact, these fields have become one of its main use cases, following the rise of Big Data and data analysis. Most of the libraries used in Machine Learning have Python interfaces.

As a reminder, Machine Learning is a subset of Artificial Intelligence. It enables patterns in one or more data streams to be discovered with millisecond precision. In other words, it enables the analysis of large volumes of data from immense information sources. It’s this new technology that gives machines the ability to learn and improve on their own. It can be used for a wide range of projects, including fraud detection, RCU (unique customer repository) and image classification.

What makes Python the ideal tool for Machine Learning?

The Python language owes its success to a number of advantages that benefit both professional and amateur programmers: ease of use, simplicity, numerous libraries, etc. Since Machine Learning requires regressive and continuous data processing, Python seems to be the tool that best meets this requirement.

Numerous learning libraries

According to programmers, Python is well-suited to Machine Learning projects because it offers numerous packages and data science libraries. These are collections of modules with pre-written code that developers use to implement functionality.

Among the most popular are :

  • Pandas,
  • Agate,
  • Bokeh,
  • NumPy,
  • Scipy,
  • Scikit-learn,
  • Matpotlib,
  • Scrapy,
  • PyBrain,
  • TensorFlow.

This diversity of libraries makes Python easier to code and saves developers a lot of time.

The simplicity of the programming language

As well as being easy to understand, Python’s syntax is also concise and readable, making it easy to write code. In this way, the programmer can test algorithms more quickly without having to implement them. In addition, this programming language has more powerful data structures and algorithms than others.

Compatibility with all operating systems

If Python is the most widely used tool among developers, it’s also because it’s compatible with the main operating systems : Windows, macOS, UNIX and Linux. It can therefore run on different platforms. What’s more, this compatibility means it can be used in a wide range of applications.

A few negative points to bear in mind

The dynamic nature of Python’s typing makes it intuitive, but it can also lead to slow speeds. As the code has to be written line by line, the execution process can take some time.

This language also has the disadvantage of using a huge amount of memory and being inefficient for programming mobile devices. Fortunately, these negative points are perfectly offset by Python’s versatility and ease of use.

Paul Maillet

Paul Maillet

Paul est un journaliste belge spécialisé dans les sujets économiques. Il travaille en tant que rédacteur et reporter depuis 20 ans. Il a publié plus de 150 articles sur le thème de l'innovation et de l'esprit d'entreprise, tant en ligne que sur papier.