Is Python the best choice for machine learning?

"Which programming language is the best?" This is the most popular and controversial question in the programming world. The answer to this question is not linear or simple because technically every programming language has its own advantages and disadvantages. There is no "best" programming language, as each language has slight advantages over the others depending on the problem. There is no doubt that Python is a highly preferred language when we talk about machine learning, but there are some factors that should be considered

We will discuss these factors in detail, but before we get into that, let’s get a quick overview of this article.

What is machine learning?

The term machine learning is pretty self-explanatory, it is a technique where a machine continuously learns and modifies its data based on the input and output variables it is uploaded to. We feed the machine different types of input and output data, and the machine generates a program or algorithm.

This is the basic definition of machine learning, but we must understand the role of Python in machine learning and its influence in this field. Python provides a large number of built-in libraries and modules that help developers generate structured tools.

The meaning of python

In recent years, Python has developed rapidly and become widely popular among developers. There are many alternatives out there, but Python remains the first choice for many data scientists and data analysts. According to a recent report from Google Search Trends -

Python is the most popular programming language with a share of 25.95%.

What’s so special about Python?

Building applications with machine learning is complex, and things get even more complicated if the chosen language doesn’t support the environment. However, this is not the case with Python as it is highly compatible with numerous external libraries. This feature allows developers to build a variety of algorithms and models. These libraries are pre-written codes that improve the readability of your program.

In machine learning, multiple models are built to predict outcomes and generate experiences from events. Based on these experiences, machines can learn new things on their own. Let’s talk about some libraries -

• "spaCy" is an open source library for deep learning. It is used for PoS tagging and speech lemmatization. This makes Python a very impressive sentiment analysis tool.

• The "NumPy" library is used to handle advanced mathematical data and allows matrix manipulation.

• "SciPy" is often used for optimization purposes in machine learning.

• "TensorFlow" is another library required for high-speed numerical calculations.

Advantages/disadvantages of using python

In order to understand which programming language is suitable for a specific task, we need to conduct a SWOT analysis. In this analysis we have to consider numerous parameters and choose the best option. Now let us compare python with other programming languages ​​-

• Runtime Parameters - In this parameter, python lags behind Java and JavaScript because python’s interpreter checks the type of the variable before performing the operation. On the other hand, in Java, the data type is already specified at the time of variable declaration, which reduces the run time. But Python is faster than C.

• Readability - Python is by far the most readable language. Python is easier to read and write because the single lines of code are short and highly similar to English. That’s why pros and beginners alike love it. For Java, JavaScript, and C, the code length is 4-12 times that of python.

• Community Health and Usage - Python’s community health is impressive due to its large user base. Since it is used by a large number of developers, it has impeccable support. There are multiple platforms to resolve errors and glitches related to different libraries.

• Compatibility and Scalability - In machine learning, developers create models, which require training and data feeds. This process is only possible if the language used is portable and supports cross-platform tasks. For such operations, Python definitely has advantages over other languages. Another amazing feature of python is the integrated environment. Python can be integrated with many other programming languages ​​such as Java, C, etc.

We have discussed several parameters and based on the comparison drawn we can say that python is a very prolific language which can be effectively used for machine learning but when it comes to being the best language , none of the existing languages ​​have it. Can completely overwhelm others.

In machine learning, each language has its own advantages and applications. For example -

Due to the large number of AI libraries, C/C is mainly used for game development. "R" is widely used in the fields of bioengineering and bioinformatics. Therefore, Python is undoubtedly very popular and efficient in the field of machine learning, but the best application is always subjective and depends on the problem faced by the developer.

in conclusion

In this article, we introduced the basics of machine learning and learned about the impact of Python programming. We discussed Python’s numerous libraries and their applications in the field of machine learning.

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