Choosing Python for your ML project: TOP-5 reasons
Machine Learning has become one of the most popular and demanded technologies due to the value it brings to companies. It can process massive data sets and analyze Big Data, draw accurate forecasts based on this data, automate business processes, power your virtual assistants and so much more. This is the reasons more and more companies adopt ML and develop innovative projects to provide their customers with an innovative solution.
To make the most out of your ML project and guarantee high quality and easy process of work, you have to choose the best tools to work with. And Python is the number one choice of many developers out there who work with ML.
We’ve collected the top-5 reasons why Python is the best option for ML — scroll down to learn more and share your opinion in comments.
The language itself
For sure, Python is comparably easy to learn programming language. It’s intuitive and elegant and has a clear and highly readable code. All these features help developers work in an efficient manner and focus on the core of the task instead of trying to figure out the code.
In addition, Python comes with a set of tools: libraries, extensions, and frameworks. This also contributes to the quality and speed of work and assists developers a lot. And because Python is general-purpose, developers can build prototypes fast and perform the tests on a regular basis for better monitoring and quality management.
Set of tools
As said above, Python comes with a vast set of tools, designed for Machine Learning specifically. Let’s have a closer look.
Here are the top-used Python libraries for ML projects:
- SciPy, NumPy: advanced & scientific computing;
- Keras, TensorFlow: machine learning;
- Seaborn, Matplotlib: data visualization;
- OpenCV: computer vision;
- SciPy: advanced calculations;
- Pandas: general-purpose data analysis.
Python has an open platform and can run on different platforms. That’s an incredibly time-saving advantage as developers can implement the task on one machine and then run it on another. Python is supported by macOS, Linux, Windows.
Another thing to mention is the fact that many companies who specialize in ML projects have their own GPUs for ML models training. Due to the available Python libraries and tools, companies can train the models on their own GPUs and avoid spending too much money and resources on finding a suitable GPU for training.
This one may seem insignificant but think about that. A big community and support mean that the language has a lot of available documentation and many people know how to solve your specific issue. As well it means it’s easier to find a qualified Python developer due to language popularity.
If we look at the Python Development Survey from 2017, we will see that Python is among the top languages for web development. And if we combine web development and data science, that would be 27% Python’s share.
As well, over 140K custom software packages are built with Python. Some of the scientific packages like Scipy or Numpy can be easily installed in a program in Python and can be used for ML purposes.
And if you are not convinced yet, some of the Python’s users are Pixar, Spotify, Google, and Celadon, of course!
With Python, developers can choose from an OOP approach or opt for scripting. Python can serve for backend purposes and for linking various data structures. It’s up to developers to choose how to use Python and the language’s flexibility indeed makes it great.
Another feature that Python offers is the option of checking most of the code right in the IDE, which is another big advantage for the developers.
Python indeed is awesome but it’s not the ultimately perfect language. As one developer said, “Python is like a Swiss Army Knife — not the fastest, not the most efficient, but a little bit of everything and of good quality”.
Indeed, in terms of speed, Julia or Scala is faster and R is easier to learn. However, Python remains a good combination of flexibility, reliability, and quality, not to mention the tools that come along.
This is why we at Celadon also prefer using Python when working on the next ML project and we highly recommend any company that works with ML to try it as well.