Due to its shorter learning curve, easier syntax, and better readability than R, Python is a great choice for beginners. Moreover, it’s a more versatile language suitable for various tasks. After gaining proficiency in it, you can then proceed to learn R. On the other hand, if you’re already familiar with the basics and aiming for tasks where R excels, choosing R as your primary language might be a better first step.
R vs Python for machine learning
Julia Granstrom
Feb 13, 2024
6 min read
content
share
Machine learning (ML) is one of the most profitable sectors of software development right now. That’s because of how useful machine learning techniques are in the rapidly growing field of data science. Data science, a field of applied mathematics and statistics, gleans useful information by the analysis and modeling of large amounts of data. Machine learning involves developing computer systems that learn and adapt using algorithms and statistical models. Applying ML techniques to data science makes it possible to advance from insights to actionable predictions.
Python is among the most popular and easy-to-learn programming languages today, and it’s widely used in data science and machine learning. That said, R is rising in popularity for its statistical computing and graphing capabilities, which are essential in data science. Today we’ll compare the benefits and disadvantages of using these two programming languages for machine learning.
Learn Python 3 from Scratch
This course focuses exclusively on teaching Python to beginners and demystifies procedural programming, grounding every new concept in the hands-on project they gradually build with the course. You will begin by understanding built-in functions for input and output, and then move on to user-defined functions. Moreover, you will learn the basic data types and their application. Next, you will learn about the various structures of programs you can write: sequential, selective, and iterative; eventually, you will apply everything you’ve learned to complete an interesting project. More than anything else, this course aims to make you a lifelong learner, and intends to act as a great start to your wonderful career in the world of computing.
What is machine learning?
Artificial Intelligence (AI) is the field of creating intelligent behavior in computers and has applications as wide-ranging as self-driving cars to natural language processing (NLP). Under the AI umbrella, machine learning is the branch of computer science concerned with systems and algorithms that perform data analysis tasks to learn and make intelligent decisions. For instance, ML algorithms help display relevant content to us on social media. They also provide insights and predictions for businesses so they can adapt to their markets faster.
The monumental amount of data in the world today, from clicks on a website to how long you look at a pair of jeans online, is called Big Data. Data scientists and statisticians perform data mining and extract trends from these datasets with machine learning to make informed decisions. The two main programming languages used for ML systems are Python and R. Next, we’ll look at both to see which is better to learn machine learning.
A Practical Guide to Machine Learning with Python
This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.
What is Python?
Python was released in 1991 by Guido van Rossum at Centrum Wiskunde & Informatica in the Netherlands. It’s a general-purpose, object-oriented programming language with a huge set of open-source data science libraries and frameworks, including Pandas, Numpy, Keras, TensorFlow, Matplotlib, SciPy, Scikit-learn, and Seaborn. For these reasons, Python is often recommended for people who want to pursue machine learning and data science. Furthermore, Python is a multi-purpose language, so you can apply it to use cases like creating web applications, workflow automation, analytics scripting, and more.
Python also has easy-to-read syntax, and this code readability makes it simpler for new users to work on a project.
What is R?
R is a programming language specifically created for statistical analysis and data visualization. It was developed by Robert Gentleman and Ross Ihaka at the University of Auckland in New Zealand. The first official open-source release of R was published in 1995 and generally replaced the S language. It’s another popular programming language, and its capital is rising with the growth of machine learning and data science.
RStudio, the most popular R integrated development environment (IDE), is available on multiple platforms. Furthermore, the rich R ecosystem has plenty of packages suitable for ML systems. For example, caret, ggplot2, nnet, and the set of packages known as the tidyverse are all available in the Comprehensive R Archive Network (CRAN). R is an especially popular choice for statistical methodology and relies heavily on statistical models.
R vs Python: Which is better for machine learning?
Python and R are both open-source programming languages with huge selections of libraries and the support of large communities. But there are key differences between them.
-
Libraries: R has a larger variety of packages specifically for statistics because of its origins in statistical models.
-
Syntax: Python has a smooth learning curve, while R, on the other hand, has a comparatively steeper learning curve. This is because of Python’s easy-to-read syntax compared to R’s complex syntax.
-
Graphics and visualization: While visualization libraries such as Matplotlib and seaborn are available in Python, R was made to present and visualize data with graphics, which means it’s much faster than Python when it comes to graphics and statistical analysis. R’s base graphics module lets you create simple charts and plots, and with packages like ggplot2 you can make more advanced displays, such as complex scatter plots with regression lines.
-
Integrations: R is also challenging to integrate in engineering environments compared to Python, although this is improving. Since R is limited to statistical analysis and visualization, it’s not an ideal choice for an ML program that needs to be integrated with a large-scale environment that fulfills a range of operations.
-
Purpose: Python can be said to be more general as compared to R, which was specifically made for statistical analysis and visualizations. We can use Python for various other purposes apart from machine learning as well.
-
Ease of learning: When it comes to the learning curve, i.e., how easy it is to learn something, Python is the way to go. Due to Python’s beginner-friendly nature and close resemblance to the English language, most people find it easier than R, which has a steeper learning curve and is focused on statistics.
-
Integrated development: environments (IDEs) An integrated development environment (IDE) serves as an application for writing and executing programs efficiently. The most popular IDE for the language R is RStudio, followed by Vim or Emacs. On the other hand, PyCharm, Visual Studio Code, and the Jupyter Notebook are commonly used when writing Python code.
At a glance, Python’s versatility makes it seem like a winner for ML. While it’s a great choice, R is quite useful for statistical analysis, and so many organizations use both languages. While you might start with just one, it could be worth learning both. For instance, you can do initial data analysis and exploration with R to take advantage of its speed, then switch to Python for shipping data products. (Python supports R functionality with the RPy2 package.)
Learn R from Scratch
The digital world we live in has given companies and people access to staggering amounts of data, and anyone that can make use of that to drive valuable insights stands to benefit. The ability to work with large amounts of data is becoming a standard requirement for countless jobs across multiple industries. Enter R, probably the most well-known language for data analysis. It's been used for years due to its robust statistical functionality, outstanding graphing ability, and extensibility through packages. This interactive course will get you up to speed and assumes no prior knowledge of R. You'll start with the very basics and work your way up to advanced concepts like exception handling. By the time you're done, you'll be able to write detailed, useful code in R yourself.
Wrapping up and next steps
In this article we discussed the differences and similarities of Python and R for machine learning. Whether you’re just dipping your toes into machine learning or building on your skills, Educative has several learning options available. If you are focusing on machine learning, Python might suit you better. In terms of industry demand, both Python and R are popular for data science. However, Python stands out for its versatility and broader application scope beyond data analysis.
For Python, the best place to start if you have some programming background is Python 3: From Beginner to Advanced. However, if you are truly starting with no Python experience, the course Learn Python 3 From Scratch can get you going.
Businesses are increasingly looking for R users. To learn more about R, the free course Learn R From Scratch uses practical examples and assumes no prior knowledge. It also introduces more advanced topics like exception handling.
If you’re committed to entering the field of machine learning, the course Become a Machine Learning Engineer, guides you through essential ML techniques with modules in image recognition, natural language processing, deep learning, and preparing for the machine learning interview.
Happy learning!
Continue learning about Python and R
Frequently Asked Questions
Should I learn R or Python first?
Is machine learning better with R or Python?
TRENDING TOPICS
