Python Vs. C++ for Machine Learning

There are many languages to choose from.  But today we’re going to talk about two of the most popular – Python vs C++ for machine learning. Let’s take a look and see how they compare.

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options. It is simple, so it’s easy to learn since it requires a unique syntax that focuses on readability.

Machine Learning is a collection of methods whose purpose is to provide software with the ability to learn. It is a type of “deep learning” that allows machines to process information for themselves on a very sophisticated level. Allowing them to perform complex functions like facial recognition. It is a subdomain of artificial intelligence. In other words, it is the practice of using algorithms to parse and learn from data. And then automatically make a prediction or “figure out” how to perform a certain task.

The big and important question to know is which programming language is the best when it comes to machine learning? If you’ve got an idea for a new project which will require machine learning capabilities. It’s important that you make the right choice, for the success (or failure) of your application will hinge upon it.



Python vs C++ for machine learning are the programming languages used for general purpose but both Python and C++ languages differ from each other in many ways. C++ is originated from C language with multiple paradigms and provide the feature of compilation.

According to a survey which was conducted last year by Developer Economics– Python is the most popular machine learning language ever. In the survey, it was found that 57% of respondents were for it, 33% prioritising it for development. And C++ came in second – 44% were using it, though only 19% were prioritising the language for machine learning development. (Taking the third, fourth and fifth spots were Java, R, and JavaScript respectively.)

We all know Python is the most popular language in the context of today. The main reason for its popularity is that it is extremely easy to learn. And is also easy to use in practice when compared to C++. It depends on the objective. To learn python you don’t need years of software engineering experience.


Python is a high-level language, and that has good and bad aspects. The good is that Python is easier to write, faster to learn, simpler to debug, and has built-in garbage collection. The negative is that it is slower because Python has to figure out how to convert the Python code into C, where and when to collect garbage, and what types different variables are. In C++, the programmer does all of this work for the compiler. Thus the compiler doesn’t have to make all of those decisions and it is faster.

Another factor to consider is the rise of GPU-accelerated computing. GPUs offer capabilities for parallelism and have led to the creation of libraries such as CUDA Python and cuDNN. What this essentially means is that more and more of the actual computing for machine learning workloads is being offloaded to GPUs. And the result is that any performance advantage that C++ may have is becoming increasingly irrelevant.


Python is well known for the concise and easily-readable code. Earning it high regard for its ease-of-use and simplicity – particularly amongst new developers. The case of c++ is not the same as of python. C++ is considered as a lower-level language. Which means it is easier to read for the computer (hence its higher performance), though harder to read for humans.

Python’s simple syntax also allows for a more natural and intuitive ETL (Extract, Transform, Load) process. And means that it is fast for development when compared to C++, allow developers to quickly test machine learning algorithms without having to implement them. So in Python vs C++ for machine learning both are equally important.

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