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Is python the best choice for machine learning?
“Which programming language is the best?” this is the most popular and debatable question in the programming world. The answer to this question is not linear or simple because technically every programming language has its own pros and cons. There is no “best” programming language because each language holds a slight advantage over other languages depending upon the problem. When we talk about machine learning, undoubtedly python is a highly preferred language but there are certain factors that should be considered
We will talk about these factors in detail but before we dive deep into the discussion, let’s quickly understand the overview of this article.
what is machine learning?
The term machine learning is pretty self-explanatory, it is the technique in which a machine keeps learning and modifying its data based on the uploaded input and output variables. We feed the machine with different kinds of input and output data and after this, the machine produces a program or an algorithm.
This is the basic definition of machine learning but we have to understand the role of python in machine learning and how impactful it can be in this field. Python offers numerous in-built libraries and modules which helps a developer to produce a structured tool.
Significance of python
In the recent years python has grown rapidly and has gained a lot of popularity among developers. There are numerous alternatives present but still python is preferred by a lot of data scientists and data analysts. According to the recent reports by google search trends −
Python is the most popular programming language with a share of 25.95%.
What makes python special?
Building applications with the help machine learning is very complex and if the chosen language does not support the environment, then things become even more complicated. However, it is not the case with python as it is highly compatible with numerous external libraries. This feature allows a developer to build several algorithms and models. These libraries are pre written codes which improves the readability of the program.
In machine learning, several models are built to predict the result and generate an experience out of an event. Based on these experiences a machine learns new things on itself. Let’s talk about a few libraries −
“spaCy” is an open-source library used in deep learning. It is used in PoS tagging and lemmatization of speech. This makes python a very impressive tool for sentiment analysis.
“NumPy” library is used to process high level of mathematical data and allow matrix processing.
“SciPy” is used frequently in machine learning for optimization purposes.
“TensorFlow” is another library required for high paced numerical computation.
Advantages/disadvantages of using python
In order to understand which programming language is suitable for a particular task we need to a SWOT analysis. In this analysis we have to consider numerous parameters and then select the best option. Now let’s compare python with other programming languages −
Runtime parameter − In this parameter python lacks behind Java and JavaScript because python’s interpreter checks the type of the variables before performing the operation. On the other hand, In Java the datatype is already specified at the time of variable declaration and this reduces the runtime. Python is faster than C++ though.
Readability − Python is by far the best language in terms of readability. Due to the short single line codes and high resemblance with the English language, python is much easier to read and write. This is the reason it is preferred by professionals as well as beginners. In case of Java, JavaScript and C++ the length of the codes are 4-12 times longer than python.
Community health and usage − Python’s community health is pretty impressive and this is due to its large user base. Since it used by a large mass of developers it receives impeccable support. There are multiple platforms that are present to solve bugs and malfunctions related to different libraries.
Compatibility and extensibility − In machine learning, a developer creates models and these models demand training and data feeding. This process can only be possible if the in-use language is portable and supports cross platform tasks. For such operations, python undoubtedly holds an edge over other languages. Another amazing feature of python is the integration environment. Python can be integrated with numerous other programming languages like Java, C++ etc.
We have discussed several parameters and on the basis of the comparison drawn, we can say python is a very prolific language and it can be efficiently used in machine learning but when it comes to being the best language then none of the existing language can completely overpower others.
In machine learning each language has its own strength and application. For example −
C/C++ is predominantly used in the development of games due to its large collection of AI libraries. “R” is heavily used in the field of bioengineering and bioinformatics. So definitely python is very popular and efficient in the field of machine learning but the best application is always subjective and depends upon the problem faced by the developer.
Conclusion
In this article we covered the basics of machine learning and understood the implications caused due to python programming. We discussed about the numerous libraries of python and their application in the field of machine learning.
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