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Python: the right language for machine learning?

In one of our previous blog articles, we already addressed the question: Why should you learn Python? One of the reasons we gave, was that this language is excellent for Machine Learning (ML). But why is that actually the case? Here we explore why Python is an excellent language for ML applications.

What is Machine Learning?

Machine Learning is a technique in which a computer program is supposed to recognize patterns and regularities on its own using existing training data. It then applies these to new data. Based on these patterns, the program can evaluate the available data and often create a forecast for future developments. Machine Learning is one of the most important technologies of the future and there is a very high demand for programmers with knowledge in this area.

What do you program with Python?

Python is suitable for many different tasks. You can use the language for common desktop programs, but it is also often used for managing processes on a server. In addition, there are many different frameworks that extend the application possibilities. With their help, you can design websites, perform scientific calculations, or even write machine learning applications with Python.

Why use Python for machine learning?

Python represents one of the most popular programming languages at the moment. Developed in the 90s as a learning language for programming beginners, Python was also frequently used for commercial applications after a short time. But why is this so? To find an answer to this, it's worth taking a look at the official Python website.

Here it says:

Python is powerful... and fast;

plays well with others;

runs everywhere;

is friendly & easy to learn;

is Open.

This quote presents the main advantages of this language. Python is a very powerful language. You can program many different applications with it. It is also fast. This does not only refer to the execution speed. The development of Python programs is also extremely fast due to its simple structures. This is one of the main reasons for its high success in commercial applications. This significantly reduces development costs. In addition, the language can be used on almost any platform. Since it is open source software, there are no fees for it. Furthermore, the language is easy to learn.

In addition to these general advantages of the programming language, there are some specific reasons why machine learning with Python is particularly easy. We will show you what these are in the following sections.

Numerous ML frameworks

Frameworks are collections of ready-made functions that significantly facilitate program development. As a rule, they are intended for a very specific task area. This way, you don't have to create the programs yourself from scratch, but can use the ready-made functions for it. This not only makes the task easier, but also reduces the effort significantly.

The Python programming language is characterized by the fact that there are many different frameworks for it. Some of them are intended for machine learning programs. Examples of this are TensorFlow, Keras and Scikit-learn.

Besides these specific ML frameworks, there are several other useful packages. For example, NumPy and Pandas are of great importance. These are intended for data analysis. This is an important foundation for machine learning. With them, you can perfectly prepare your data for machine learning applications.

All these frameworks ensure that the effort required for ML programs is extremely low. This means that you quickly achieve high-quality results. Moreover, this makes it particularly easy to get started.

Functional programming

There are different programming paradigms in computer science. One of them is functional programming. Here, functions have a special significance in program design. This programming paradigm is ideally suited for machine learning and other applications from the field of artificial intelligence. The reason for this is that the structures that are used for ML applications can be represented particularly well with functional programs. The approach here is usually to break a complicated problem into simpler sub-areas and process these separately. This is exactly the same approach as in functional programming. Therefore, this technique is an excellent fit for machine learning.

Not all programming languages support functional programming. Python is primarily known as an object-oriented programming language. However, it is also suitable for the functional style. This represents another important reason why it is very popular for machine learning applications.


ML applications often run on very different computers, on a PC, on a server, or on mainframes. That's why it's very convenient that Python is a platform-independent language. You can easily run your programs on different devices.

Great community

Python is one of the programming languages with the largest community. This can be very helpful if you get stuck with your programs. You can describe your problems in various forums. There are many experienced programmers who are happy to help you.

Why learn Python?

You're still asking yourself: why learn Python? The good suitability for machine learning programs is certainly an important reason. Already, the demand for programmers in this field is very high. It is expected to become even higher as ML applications continue to grow in importance. If you want to learn this technique, Python is exactly the right programming language.

The best ways to learn Python

If the benefits of Python have convinced you and you want to start learning the language, there are several methods to choose from such as books, online courses, or Code Pirate's coding app. We've already discussed which is the best method for learning programming in the linked article. The result is quite clear: The app from Code Pirate offers many advantages. This offer is very cheap and effective. With it, you can learn programming at any location and achieve optimal success.

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Python: the right language for machine learning?

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