Machine learning is one of the most up-to-date branches of technology in the modern era. According to Forbes, patents filed by machine learning grew by 34% between 2013 and 2017, and this trend is set to increase in the future. Python is one of the main programming languages that is currently mostly used for research and development of machine learning.
According to the results of Google Trends, the interest in Python for machine learning has increased significantly compared to other programming languages such as R, Java, Scala, Julia, etc.
While Python is currently the best programming language for Machine Learning and has also become the most widely used programming language for scientific computing and artificial intelligence, in this article we will point out the reasons for the advantage of machine learning with Python.
Table of Contents
1) Python is easy to use
No one likes things that are too complicated, so Python’s ease of use is one of the main reasons for its popularity in machine learning. The language has a very simple structure and commands that are easy to read, which makes both programmers and experimental students like it. Python’s simplicity means developers can focus on solving core problems and training machine learning instead of spending all their time and energy on the technical nuances of programming languages.
In addition, Python is very efficient and enables programmers to do more with fewer lines of code. Also, Python codes are easily understood by humans due to their high similarity to human language, and this makes Python ideal for building machine learning models. However, why go to another language for machine learning!?
Machine learning is still in its early stages around the world and there are many projects to be done and many things to improve. When you start working on your ideas in machine learning projects, you not only can test your strengths and weaknesses but you will be exposed and it can also be a good opportunity to strengthen your career.
2) Python has several libraries and frameworks suitable for machine learning
Python is very popular now and as a result, it has hundreds of different libraries and frameworks that can be used by developers. These libraries and frameworks are very useful in saving time, which in turn makes the Python language popular.
Many Python libraries are specifically written for artificial intelligence and machine learning and are very effective in this field. Some of these libraries are given below:
Keras is an open-source library specifically focused on testing deep neural networks.
TensorFlow: Tensorflow and Pytorch are free and open-source libraries that have various applications in machine learning. These libraries are used for implementations related to “Neural Networks” and especially “Deep Learning” as well as “Tensors” calculations.
Scikit-Learn: “Scikit-Learn” library is a free software library for Machine Learning that has different classification, regression, and clustering algorithms. Also, Scikit-Learn can be used in combination with NumPy and SciPy.
Seaborn: This library is another tool for performing visualizations, except that it focuses more on statistical visualizations. Items such as “Histogram”, “Pie Charts”, “Curves” or “Correlation Tables” are among the items that can be implemented using this library.
Matplotlib: After the user has saved the data as a “Data Frame” using the Pandas library, he will need some visualization methods to understand the existing data. Images are usually better and more expressive than the data itself (especially for end-users who may have different expertise and numerical statistics and textual analysis may not be good options for providing output to them). “Matplotlib” is a powerful library for data visualization that can be used to draw various graphs.
NumPy: NumPy is a famous library for numerical analysis. This library helps the user to perform many tasks from calculating the median and distribution of data to processing multidimensional arrays.
Pandas: To process a CSV file, you can use the Pandas library. Of course, in this regard, the user needs to process several tables and view their statistics.
3) Python has many supporters
Python has been around since 1990 and has had enough time to build a supportive community. Because of this support, Python learners can easily improve their machine-learning knowledge, which leads to its increasing popularity. There are many resources on the Internet for machine learning and its libraries in Python, from machine learning tutorials From GeeksforGeeks to YouTube tutorials that have been of great help to language learners.
Also, corporate support is an important part of Python’s success in machine learning. Many top companies like Google, Facebook, Instagram, Netflix, Quora, etc. use Python for their products. Google is responsible for creating many Python libraries for training machine learning such as Keras, TensorFlow, etc.
4) Python is portable and expandable
This is an important reason why the Python programming language has become so popular in machine learning. Many cross-language operations are easily done on Python due to its portable nature (that is, a program written in Python runs the same way on different computers with different hardware) and extensibility. Many data scientists prefer to use graphics processing units (GPUs) for their ML models on devices, and the portable nature of Python is well suited for this.
Python is also supported by many different operating systems such as Windows, Macintosh, Linux, Solaris, etc. Furthermore, due to its extensive nature, Python can be integrated with Java, .NET, or C/C++ libraries.