It’s important to realise that there is no one “best” language before exploring the many machine learning languages. Each has a distinct set of benefits and drawbacks as well as special skills. What you are trying to build and your background are the main determinants.
Having said that, Python is without a doubt the machine-learning language that is used the most. 57% of data scientists and machine learning experts say they use Python, and 33% prioritise its development.
Python frameworks have significantly improved recently, expanding their capacity for deep learning. Top libraries have been made available, including TensorFlow and others.
With good reason, more than 8.2 million developers use Python for development. It is a well-liked option for artificial intelligence, machine learning, data science, and data analytics. Practitioners of machine learning may quickly access, handle, edit, and process data because to its comprehensive library environment. Additionally, it offers increased readability, reduced complexity, and platform independence.
Machine learning engineers do not need to start from scratch because the built-in frameworks and modules offer fundamental capabilities. Additionally, because machine learning requires ongoing data processing, Python’s built-in tools and packages may assist with almost every task. When using sophisticated machine learning technologies, all of this leads to a reduction in development time and an increase in production.
Many of the biggest internet businesses in the world, such as Google, Instagram, Netflix, Walt Disney, Facebook, Dropbox, YouTube, Uber, and Amazon, favour Python as their preferred programming language.
Although Python is undoubtedly the most well-known language, there are a few options to take into account. The top five languages at the moment are Python, R, C/C++, Java, and JavaScript. Python is frequently regarded as being far superior to C/C++. Although Python and R are occasionally compared, Java is not far behind, and the two do not compete in terms of popularity. R continually has the lowest priority-to-usage ratio among the languages according to surveys of data scientists. The last item on the list is often Javascript.
Other languages used by machine learning practitioners that are worth taking into account but are less well-known than the top five include Julia, Scala, Octave, Ruby, MATLAB, and SAS.
The type of project you’ll be concentrating on or your specialised applications are the most important factors to take into account when choosing the best language for machine learning.
Python or R are your best bets if you want to work on sentiment analysis, while Java is better for network security and fraud detection. Large companies regularly employ network security and fraud detection methods, and Java is frequently chosen for internal development departments for these purposes.
Python offers a simpler and quicker alternative for algorithm development in less enterprise-focused disciplines like natural language processing (NLP) and sentiment analysis because to its extensive library of specialised libraries.
On the other hand, C/C++ is frequently utilised for robot movement and artificial intelligence in video games. The machine-learning language offers a high level of control, efficiency, and productivity due to its extremely sophisticated AI libraries.
R has long been used in medical statistics, both inside and outside of academia, and it has recently gained popularity in the fields of bioengineering and bioinformatics. However, JavaScript is typically selected by developers who are new to data science and machine learning.
