C++ or Python: Which Programming Language Should ML Developers Choose?

This article will discuss C++ or Python, which language is best for ML developers

In recent years, machine learning has taken the high-tech world by storm, and C++ or Python for machine learning are two tools that can help you get from point A to point B in this regard. Then, what can we expect? Well, there are a lot of impressive uses for machine learning, but among the top ones are fraud detection, industrial automation, and speech and image recognition.

Of course, many cool products are enhanced by machine learning, too. They are frequently created by cutting-edge companies, trendsetters, and eye catchers. Among them are Twitter, Pinterest, Google, IBM, Salesforce, and Amazon. Some of the most fascinating machine learning applications carried out by those corporations include removing spam, polishing chatbots, enhancing prediction and ranking algorithms as well as providing correct treatment recommendations for patients. Naturally, premium or well-known brands are not the only ones who work with machine learning in C++, Python, or another computer language. Research and analytical initiatives of varied scopes are two examples of more typical and practical applications. Here is our brief guide if you are debating between C++ and Python for your upcoming ML project.

C++ for machine learning in brief

Although C++ ML is an option, Python is actually what comes to mind when we discuss machine learning. This programming language's enormous popularity, which is due to its simplicity, power, and ease of learning, has contributed to the development of fields like artificial intelligence, machine learning, and data science. Python is incredibly simple to learn and powerful enough to continue using in many applications, even those that are difficult. However, C++ is also important to this narrative. As it is called more frequently and is thought to be more effective, it might be quite helpful.

Furthermore, even though numerous Python libraries are used in machine learning projects' development, these projects' core code is frequently written in C or C++. Any of them can be considered as a C++ machine learning library as a result. Anyhow, those performance benefits of C++ are typically inaccessible to average programmers and are only applicable to use cases requiring a higher level of sophistication. In terms of analytical or research applications, Python is unquestionably the solution. And when it comes to creating new algorithms, C++ may be the best option.

Machine learning with C++ or Python – comparison

Without a doubt, there are several facets to the problem of C++ machine learning. According to some estimates, 90% of programmers' effort is spent creating Python code for AI applications, compared to 99% of CPU (or processing) time spent writing C or C++ code. We might anticipate a strong performance if we choose to use C++ in machine learning (for instance, with a library for linear algebra). Although C++ can run significantly quicker than Python, it is more sophisticated and contains more traps than Python. Writing code and debugging in C++ is therefore more difficult and time-consuming.

Even sophisticated machine learning algorithms may be quickly and readily evaluated with Python, allowing a software developer to move forward with their job without any problems. Additionally, since Python is widely used in machine learning, many contributions, such as well-known ML models, can be partially reused, updated, or used as a reference for subsequent work. On the other hand, projects involving embedded systems and robots may benefit more from the use of C++.

Choose the best technology for your machine learning project

Regarding machine learning using C++, it's important to keep in mind that while C++ may perform faster than Python, it has a much steeper learning curve. Sometimes a decade is insufficient to completely understand all of its nuances. The ability to use C++ or C libraries in Python allows users to benefit from the performance of these languages' machine learning tools without having to write any code. NumPy, SciPy, Jupyter, Dask, Scikit-learn, Pandas, PyTorch, and TensorFlow are a few examples of the astounding number of Python libraries or Python-based ecosystems ideal for machine learning. Whatever you decide, machine learning is unquestionably the wave of the future, so it's worth a go.