Known for its versatility and stability, Python is increasingly becoming an object of interest for those dabbling in machine learning or willing to carry out a machine learning project. As they quickly notice the difference between a standard software development project and an ML one, they search for tools and solutions that will respond to ML-specific needs.
Here’s where Python comes into play. It makes use of the solutions that Python users may already know from other languages such as Java, C++, or JavaScript, encouraging them to use it for new, ML-related purposes. It also offers a robust programming environment that gives plenty of opportunities to both proficient Python users and newcomers willing to make their ML projects more flexible.
But what exactly makes Python and machine learning such a great match?
We sat down with Łukasz Eckert, an expert machine learning engineer at STX Next, to try to answer this question and convince you that using this language for developing your ML project is an attractive option for anyone in charge of picking the right tools for the project—be it an engineering manager or a tech lead.
We will also briefly discuss Python’s value for a software developer who simply wants to broaden their horizons and find new opportunities while working on an ML project.
In this article based on our interview, we will look at the general value that Python offers for ML and its advantages as well as possible disadvantages. We will also provide you with some suggestions on how to include Python in your ML toolset, using some helpful resources and advice given by our expert.