Python is the most popular language for machine learning, but does it mean it is the best? Today there are more and more followers of less popular languages, such as, for example, Go. If you are wondering which one to choose — this article is for you! Here is a comprehensive comparison of these two languages that will allow you to make the right decision!
Originally published at https://websensa.com on July 1, 2020.
Why machine learning?
AI and ML-based solutions become increasingly more popular. However, the Stack Overflow Developer Survey 2019 results say that only 7.9% of programmers in the world specialize in big data and machine learning!
Machine learning is the future, and soon many companies will look for employees, who are ML engineers. Finding them will not be easy, so companies will have to compete for the best specialists on the market. And that means a lot of exciting job offers in addition to vast salaries for machine learning engineers!
AI is one of the most important things humanity is working on. It is more profound than electricity or fire. — Sundar Pichai, Google CEO
The most popular programming languages for machine learning
Before we move on to comparing Go with Python, let’s check which programming languages are the most popular with machine learning engineers.
Below you will find a list of top 10 programming languages used for machine learning which was published on Github:
As you can see, we decided to compare a real giant among machine learning languages with a very niche language. So, let’s see what comes of it!
Python vs. Go: Key Differences
We already know that Python is a much more popular machine learning language than Go. Now it’s time to compare factors such as usability, profitability, and the main pros and cons of these programming languages.
Python is a general-purpose programming language.With an easy-to-use syntax, it is one of the best programming languages to learn for beginners. It is also very popular and has a huge number of various libraries. One of its main features is universality because it can do almost every conceivable task.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. — www.python.org
What is Python used for?
First of all, it is worth noting that Python is a universal language. You can do almost anything with it — from a simple website to an advanced application!
Python is used for:
- web development
- desktop applications
- data science
- artificial intelligence and machine learning
- Internet of things
- game development
- web scraping & web crawling
Today Python is the fastest-growing programming language. According to the results of Stack Overflow Developer Survey, it is the third most popular technology used by software developers.
Moreover, it is also the second most loved programming language among IT specialists.
According to Stack Overflow, the average annual salary of a Python developer is $63k. Which puts it on the 12th place on the list of the top-paying technologies.
Why is Python used for machine learning?
Machine learning requires continuous data processing, and Python is perfect for working with large datasets. Furthermore, due to the huge amount of analyzed data in ML, it’s necessary to create solutions that will be both effective and simple. For this purpose, Python is the perfect solution because its structure is easier to use than other programming languages.
Moreover, the syntax of Python is really simple. Many people who want to become a machine learning engineer and do not yet know any programming language start their learning with Python. And it is an excellent choice!
One of the essential reasons why Python is good for ML is a vast library ecosystem. It has a lot of useful machine learning libraries such as:
- TensorFlow — is one of the best libraries for working with ML on Python. It is a scalable, flexible, and fast open-source solution used for research and production.
- Keras — is an open-source neural network library that extends the usability of TensorFlow with additional features for ML and DL programming. Engineers use it for effective and fast datasets processing.
- Scikit-Learn — easy use and flexible library focussed on data modeling. It allows for easy integration with different ML programming libraries.
These are just a few of the huge collection of available Python libraries for Machine Learning.
Lastly, it is important to highlight that Python is also flexible, which enables choosing the programming styles. We can even combine a few of them to solve various types of problems in the most effective way.
Pros and cons of using Python for machine learning
- a low barrier to entry
- popularity and large community
- a huge number of frameworks and machine learning libraries
- highly rated machine-learning repositories
- speed limitations
- weak in mobile computing
- underdeveloped database access layers
- a very few statistical model packages
Go (also known as “ Golang “) is a comparatively new programming language, launched in 2009 by Google. Its syntax is similar to C, so individual developers say that it is the C for the twenty-first century. Despite lacking the popularity of Python, Go has a lot of followers who see its great potential.
Go is an open-source programming language that makes it easy to build simple, reliable, and efficient software. — https://golang.org
What is Go used for?
Go is a flexible language, and it can solve a lot of various problems.
You can use Go for:
- web development
- command-line scripting
- multimedia editing
- big data
- machine learning
- network server applications
- cloud services
Go is a relatively young language, so it is also less popular than Python. According to Stack Overflow, Go ranked in 13th place among the most popular technologies.
According to Stack Overflow, Go is the third most highly-paid language overall. The average annual salary of a Go developer is $80k. That’s much better than Python!
Why is Go used for machine learning?
First of all, Go has a lot of machine learning tools, which are built into its library. It also has native support for these tools that makes the developer’s work faster and more effective.
Go is great to use when you try to distribute your project across platforms. Go compiles down to a single binary, and it doesn’t require dependency management and additional engineering effort. This process is much easier and faster than in the case of Python.
Moreover, Go is also easy to learn. It has clean, accessible syntax easy to newcomers, and there are not a lot of functions to discover. Interestingly, according to the opinion of individual developers, it is even simpler than Python!
What’s essential, this programming language is also fast (much faster than Python) and handles concurrency well. Go compiles down to a single binary so that it could be the right choice for machine learning.
Pros and cons of using Go for machine learning
- compiled — it doesn’t require any interpretation
- great for infrastructure projects
- automatic documentation and embedded testing environment
- a smart standard library
- strong security that’s built-in
- lack of versatility
- lack of a virtual machine
- relatively few libraries (especially ML libraries)
Python vs. Go — which one is better for machine learning?
We already know the pros and cons of Python and Go. So, it’s time to answer the critical question — which programming language is better for machine learning?
Let’s start with the common features. Both languages have simple syntax and are easy to learn for beginners. Both are also very flexible, and they can be used in almost any project.
And finally, both have far more advantages than disadvantages.
And what about the differences? First of all, Python is older and more popular. The greatest competitive advantage of this language is the very extensive range of various useful machine learning libraries. However, it is also significantly slower and more prone to bugs than Go.
Go is not as popular as Python, but it has a lot of followers who see its great potential. It is growing fast, so it may be a serious competition for Python in the future. Today we have to admit that Go is faster, more scalable, and performant so that it is perfect for large-scale projects. And that makes it one of the best choices to work on machine learning infrastructure.
In conclusion, if you want to develop ML-based solutions, knowing and using Python is a requirement. It is currently still one of the best choices to develop machine learning algorithms. However, if you want to actually run a production machine learning API at a bigger scale, you need professional infrastructure. And in this case, Go is probably a better solution.
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Originally published at https://websensa.com on July 1, 2020.