Are you a Machine Learning practitioner or a wannabe Machine Learning engineer? Maybe you are wondering what programming languages to learn or how to upskill for a job in Machine Learning and data science?
Well, you have landed at the right place.
Register for this free course and learn Machine Learning with R. Get familiar with the basics of Machine Learning and how to use R for developing Machine Learning projects. Learn why R is emerging as a popular choice, and understand the criteria for choosing between the R and Python languages.
Machine Learning is a branch of Artificial Intelligence that trains a machine to learn on its own. It is the branch of study that gives computers the ability to learn without human intervention or being explicitly programmed.
Machine Learning uses algorithms to build models and discover patterns and relationships so that organizations can make decisions.
Those businesses working with large data that want to develop self-learning models are using Machine Learning applications for business advantage. For instance, banks and financial institutions use Machine Learning for data-driven insights for business opportunities and prevent fraud.
So if you are thinking of building a Machine Learning project and wondering which programming language to choose, this article will help you clear your confusion. Understand the pros and cons of both languages before you decide which one to use.
Also Read: What is Computer Science? Everything You Need to Know About a CS Career
Python is a general-purpose, interpreted, interactive, and object-oriented popular programming language. Being general-purpose, Python can create many different programs and is not necessarily customized for one particular problem. It uses English keywords with few syntactical constructions and is highly readable. Python has an interactive mode that permits interactive testing and debugging of code snippets.
Python is used to create web applications on a server. It is used to build websites and software and for the automation of tasks and data analysis.
Python is necessary for students working in web development or software engineering. It is ideal for newbie programmers as it supports the development of many applications, from text processing to browsers and games.
Why is Python popular? It makes for a great first programming language. It has the support of a large programming community and excellent online documentation. It has a smooth learning curve. Python has many bundles of libraries and packages to choose from, some of which are tailored to particular tasks. In any engineering setting, the integration by Python records faster than that of R. The syntax is easy to decipher, which ensures high profitability in development. Lastly, Python is a powerful language, and its flexibility makes it easy to use and apply in any technology or purpose like in this instance, Machine Learning,
R is a programming language that supports statistical computing, graphics, and reporting. It is also considered a software environment for statistical techniques like linear and nonlinear modeling, classical testing, clustering, classification, etc., and graphical displays either on the computer or as print-outs, making it the preferred choice for statistical analysis.
R is based on an interpreted computer language that supports branching, looping, and modular programming using functions. An advantage of R is its integration with the processes in C, C++, .Net, Python, or FORTRAN languages for enhanced operations. R enables efficient data handling and storage. It provides a suite of operators for calculations and a large library of dedicated packages and tools for analysis. As R has computations based on statistical principles it is a good choice for innovative projects or those with a statistical underpinning.
Because of R’s capability to handle a massive amount of data and the support of a vibrant contributor community, it is the choice of data scientists. R is thus well suited to exploratory tasks and data analysis.
Both Python and R have pros and cons but, Python emerges as the winner for building Machine Learning projects because it is easy to write and understand, being a fully-featured general programming language. It also has stacks that host libraries dedicated to Machine Learning, like the scikit-learn. The Keras library written in Python is preferred by Machine Learning engineers who want to create and evaluate deep learning models.
Both are interpreted languages with robust IDEs, but Python is easier to learn while R requires studying its scripting language.
In speed, R wins hands down, especially with its enterprise-ready versions. Python is slow. So if speed is your criteria, then opting for R is the right decision.
In visualization too, R is the right choice, especially where you need to plot data or visualize patterns.
Although R has more than 5000 libraries, Python has more dedicated packages like NumPy, SciPy, Pandas, Matplotlib, etc., which makes Machine Learning projects easier to develop and implement.
R stores data in RAM which limits data handling capabilities when handling big data.
As both Python and R languages are equally good, it largely depends upon what you want from your Machine Learning project. Are you handling huge data? Is speed critical? Do you want to develop a model on an ad-hoc basis? How do you plan to visualize your data?
Ultimately, the domain and the company IT environment factors when choosing the right programming language.
Both R and Python have similar features, are open-source and free. Both have advantages for Machine Learning projects, yet Python lends itself better to data manipulation and recurring tasks typical in Machine Learning iteration. So it makes it the right language to build a Machine Learning project. However, if you want to develop a tool for instant analysis, R is a good choice.
It is observed that business users and data scientists who work in retail and marketing, for instance, prefer R language over R for its visualization.
So study your company needs, present or future. And make yourself job-ready by learning the R language for your Machine Learning tasks.