Everything you Need to Know: Why Python is Most Popular Language for Machine Learning
Artificial intelligence technologies such as the Internet of Things are gradually changing the world. The rise of these technologies stipulates the growing demand for machine learning engineers, specifically, Python developers, because today the language is considered the best programming language for machine learning. In this article, we will discuss Python as an integral part of the machine learning technology stack and find out why is Python so popular in this domain.
The Popularity of Python Programming in 2018
Why do so many technology companies want to hire Python programmer? This versatile, easy-to-understand language is a perfect fit for beginner software engineers. At the same time, it is great for solutions of the complex problems. The recent 2018 Developer Survey by StackOverflow reports that Python is the most wanted language as of 2018. Therefore, Python ML library developers are in high demand nowadays. For example, let’s take a look at the vacancy trend chart for Python developers by ITJobsWatch.
According to the chart, in 2018, 1% of all vacancies posted in the United Kingdom are those of Python programmers, as compared with only 0.2% in 2010. If we speak about the United States, in September 2018, an average salary of a Python developer has been equal to $117,623, as per Indeed.com. Finally, according to the TIOBE index as of September 2018, the language has ended up in Top 3 for the first time in its history, in comparison with Top 5 in September 2017.
What Can You Do with Python?
These are the areas where the language is commonly used:
● Application development. Python makes it possible to build both desktop and mobile applications. PyQt framework, an improved version of the Qt framework, is one of the best Python libraries when it comes to desktop app development.
● Web development. Django and Flask web development frameworks power many websites nowadays, among which are Quora, Instagram, Bitbucket, and Pinterest.
● Automation. The language is a perfect tool to automate some routine tasks, such as copy/paste actions or test cases.
● Data science. Python’s packages and snippets are applied in various data science use cases, from face and voice recognition to computer game scenarios.
● Machine learning. Offering a wide range of ML libraries, the language is considered a leader in the ML domain. According to the Skill Up 2018 eBook by Packt Publishing, it tops the list of ML and data science languages: 77% software engineers indicated that they use this language for ML tasks. Moreover, 8 out of the 10 most used data tools are Python libraries.
Python ML Library Collection
One of the reasons why is Python so popular is its extensive collection of ML libraries. Here are some of the native Python ML packages that are included in Anaconda IDE:
● Tensorflow: Tensorflow is an open source library that Google applications such as Google voice search use for ML purposes.
● NumPy: Knowledge of NumPy is a must for those who are thinking of how to become a machine learning engineer. NumPy’s interface is typically used to express sounds, images, linear algebra operations, and other raw binary streams that are used in ML.
● SciPy: This is a library for technical and scientific computing. Its modules can handle tasks such as interpolation, FFT, linear algebra, signal and image processing, ODE solvers, etc.
● Scikit-learn: This library features the classification, regression and clustering algorithms, and is designed to work together with NumPy and SciPy.
● Pandas: This library is great for dealing with numerical tables and time series as well as handling data structures.
● PyBrain: The PyBrain package offers various algorithms for ML tasks and gives an opportunity to test those algorithms in specific environments.
● Matplotlib: This package is used for visualization of data with the help of 2D and 3D graphs (plotting).
Of course, this list is not extensive – there are thousands of open source ML libraries for Python, with the new ones being released on a regular basis. For example, a search for “machine learning Python” on GitHub returns more than 12,000 results.
So Why Use Python in Machine Learning?
In addition to a vast number of pre-built ML libraries, other important reasons for such popularity are as follows:
● Simplicity. It is simple and easy to learn, so not much time is needed to discover the new talents in the ML domain.
● Time-saving. As an object-oriented language, it provides user-friendly data structures, thus saving the time and effort of ML engineers.
● Flexibility. It is platform-independent, making it possible to launch an ML project across different operating systems.
● Open source nature. As Python’s binaries are freely available to everyone interested, developers can release new ML libraries for this language.
● Community. There is a huge community of Python developers that can help each other fix various issues and give advice on how to use Python for machine learning.
Machine learning is the hottest trend these days, with Python as a prevailing language in this domain. As of 2018, more and more companies are opening new positions for Python programmers. According to surveys, it is now the world’s third most popular and most wanted language.
Python is widely used for app development, web development, automation, data science, and machine learning. 77% of developers use it for ML due to the following reasons:
● a big amount of native and third-party ML libraries
● time economy
● open source nature
● a big community of Python programmers