Last Updated on March 13, 2020
Anne is a Machine Learning enthusiast, and throughout in her 25 years of programing life, she was coding on Python. She thought there could be no other programming language better than Python for making machines understand how to learn.
In my conversation with Anne when I asked, what is Machine learning?
“Machine learning is a subset of Artificial Intelligence, which, in simple terms, is a science of getting things done by computers without directly programming. This way, the machines(computers) learn on their own by studying algorithms and statistical models.”
She was in love with her life, enabling machines to learn; because she had the best tool in hand- Python. The incredible benefits she accumulated by choosing Python proved to be vital. If you are wondering, “Why Python is best for Machine Learning?”
Here are the top 3 benefits of using Python for ML.
Why Use Python for Machine Learning?
1. Simplicity of Consistency
Python is a highly readable language. It is simple for the human mind to understand, and hence it becomes easy to use Python for Machine Learning. Developers find Python easy to learn, so they prefer to use it over any other programming language.
For a big complicated machine learning project, Python is highly suitable because it allows collaborative implementations from multiple developers like Anne. Moreover, it is a general-purpose language, so a set of machine learning tasks can make use of it, and developers can build prototypes to test their AI products.
2. Exclusive Libraries
The most prominent benefit of using Python is the availability of extensive support libraries. Specifically, for AI and ML, Python has the following:
- Keras, TensorFlow, PyTorch, and Scikit-learn libraries for machine learning algorithms
- NumPy library for multi-dimensional arrays, matrices, and advance mathematical functions
- SciPy for linear algebra, optimization, integrations, and stats
- Pandas for general-purpose data analysis
- Seaborn and Matplotlib for data visualization
Such tremendous support eliminates the development time and overhead for programming, providing ease to Anne.
3. Established Community
Python has a vast ecosystem, and it has the OSI-approved open-source license, which makes it free to use and distribute. The vibrant and active community members are continuously collaborating by creating new libraries, updating documentation, and extending the toolset.
The Things transformed, obviously for a reason
Anne was in total trance using the best programming language for Machine Learning. Then suddenly, something happened when she got to know that Golang is going to overpower Python. All her thoughts and beliefs regarding Python changed. It happened as she came to know about the Golang programming language (Go). Golang is comparatively new. It was launched a decade back in 2009 by Google. NOT because Python is an older one, but just because of Go’s advancement.
Why Should You choose Go over Python?
Go is a compiled language, and there are significant advantages of Gloang development services when Anne gets to know about developers’ new programming inclination towards Go, she tried her hands on this new programming language for ML, and the results were outstanding.
The main plus-point of Go is its simplicity. Anne thought that Python was simple & easy only until she used Go because Go is simpler than Python.
Golang is a compiled language, and it compiles into a single library. It is a statically typed language, and it links all the dependency libraries and modules into a single binary file.
Here, developers like Anne need not install dependencies on the server. Instead, she just had to upload a compiled file for her app to start working.
Concurrency and Faster performance
Go uses goroutines for concurrency, which is a resource-efficient way to save CPU and memory. Anne is super-impressed by the faster performance of Go as it saves costs and resources.
Most of the tools needed for ML are already in-built into the Go library, so developers don’t look for third-party libraries. Go programming language has superficial native support for tools that speed up the entire process of app development. Also, the Go community is getting strong and reliable for any kind of help.
IDE & Debugging
A significant advantage of using Go is getting a top-notch Integrated Development Environment (IDE). Anne and most developers feel that in an agile, competitive world, an IDE is the most crucial aspect for development as it can speed-up or hinder the process. Golang comes with a comprehensive IDE and excellent debugging tools and plugins.
The Go language has a precise syntax, which makes it easy and straightforward. No unnecessary components are holding back the developers on the structure. Instead, development becomes direct and clear.
Knowing such ease and usefulness of Go, Anne thought, Go seems to be an ideal choice for machine learning development services and she started implementing ML. And here’s what she found:
What are the Benefits of using Go for Machine Learning?
1. AI Libraries
As mentioned earlier, Golang doesn’t require additional libraries; it writes everything in core. Though it provides lesser libraries, those specific libraries cover a broad scope of purposes. The GoLearn library covers data handling; Hector covers binary classification problems; Theano is similar to TensorFlow, and Goml for passing data.
However, Go still needs to work on its toolkit development. It is still in progress and has already set up a good community on GitHub.
2. Exceptional Computation Speed
Math computations are like cake pieces for Go. Unlike Python, large-scale projects are better suited to Go owing to its scalability and performance. When it comes to complex AI computational math programs, Go performs 20-50% better than Python. This way, Anne was utterly taken by the charm of Go.
3. Minimalistic & Readable
Go follows the minimalistic approach for its AI and ML algorithms. After the implementation, developers of Go create very readable code because of this minimalistic approach.
Henceforth, both languages have their pros and cons. Anne hopes that this comparison will be helpful to you while developing your Machine Learning Solution. If you are looking for assistance for Golang and wondering what other possibilities could be discovered for machine learning, then hire Golang developer from us to build the next-gen-enterprise application.