When asked about the features that make Python an excellent language for machine learning, Łukasz Eckert offers a surprisingly candid answer: “Python is good, because there is nothing better at the moment. All right, there is also R, a programming language that has an academic background and is also used for machine learning, but it is used mainly at universities. It is generally agreed that apart from R and Python, there is simply not much to choose from.”
That doesn’t mean, however, that Python’s reputation as an efficient machine learning solution is based mainly on its apparent inevitability. As an iterative process, machine learning requires a frequent revisiting of all steps in the project lifecycle and changing things along the way.
Thanks to its flexibility, Python supports this process. “When completing a machine learning project, we can’t sit down and say, ‘We’re gonna do this, this, and that.’ We repeat certain processes until we get to the desired confidence level.”
Python addresses this need for reiteration by allowing us to introduce changes during the development process. “We can declare one variable as type A, but later, we can change it to type B. If these two types ‘implement’ a common interface, everything works and nothing else needs to be changed. With Python, there is no need to declare the base interface explicitly and this greatly speeds up prototyping,” Łukasz explains.
Another feature that adds to Python’s flexibility is its ability to expand using other languages. Using the CPython interpreter makes it possible to extend Python code by means of code written in other languages, such as C# or C++. It also allows for creating system-specific Linux libraries.
Relatively easy and intuitive use as well as its well-developed environment makes Python an unparalleled machine learning tool. “Python has grown so large and has already addressed so many aspects of ML that we’re now witnessing a snowball effect and can get ahold of a multitude of Python-related resources. This makes for a highly comprehensive machine learning framework. It’s hard to imagine that it might become easily replaceable,” Łukasz adds.
Python responds to the needs of machine learning engineers by giving them opportunities to implement any required changes and ideas on an ongoing basis. In ML, challenges pop up as you continue working on your project, so reworking things is something you may expect.
Let’s say you realize you should have treated your output data a bit differently. You may think that it is too late now to introduce any changes, but with plenty of useful libraries helping you do whatever you need to, Python again shows both its ability to integrate with other resources and its adaptability to users’ needs.
Its stability and syntactic consistency make it easy for you to work with the language and write code that is readable and concise.
Those interested in high processing speed may find Python a little slow. By nature, Python is not a fast language, but that’s the case for a reason—the same design choices that make it flexible and user-friendly have an impact on its performance. Therefore, using it may prove challenging if you want to do several things at once.
One thing that has a negative effect on Python’s performance speed is GIL (Global Interpreter Lock), which is considered the main obstacle to multithreading in Python. “If there’s anything I would gladly get rid of as a Python user, it’s GIL,” Łukasz laughs.
But is it possible? Over the years, several attempts have been made to remove GIL. However, those came at the cost of lowering the single-threaded and multithreaded performance, slowing down the already existing Python applications. It is likely, though, that the general efficiency of Python helps its users accept the GIL-related inconveniences in the process.
When you start a machine learning project, how easy is it to use Python for it? The usual answer, which you can find in many beginners’ guides to Python, is: quite easy.
What those same guides usually fail to tell you, however, is that this language may be a trap for some inexperienced ML engineers.
“Python hides a lot from you,” Łukasz says. “I started programming as a C++ and C user. Those languages may not seem very user-friendly, but at least they show you a lot of things that Python doesn’t. Then the question is whether we will have to worry about this at a later stage of writing code; we usually don’t, but we should be aware that it may happen.”
Putting that aside, Python is generally considered a simple language to learn thanks to a huge number of useful libraries that you can easily connect with it. Python is also intuitive to use, which allows you to start writing code quickly and encourages you to explore its various functionalities.
If you’re about to begin your ML adventure with Python, you may already know that there are plenty of recommendations on what to read or use first. To get some idea of the basics, it’s definitely worth getting familiar with Pandas—a popular library for ML and an essential data analysis tool.
Learning how to use the Pandas package is usually what you start every Python for ML course with. It’s used for data cleaning and analysis, and is a great go-to tool for tabular manipulations and dealing with non-obvious cases. Pandas allows you to read and process files like CSV (comma-separated values), Excel, and other file types, making them easier to work with.
A useful package in a beginner’s toolkit is also NumPy. It’s a set of functionalities that helps you work with numerical data, e.g. perform matrix and vector operations. A good set of tools for beginners also includes Matplotlib or any other plotting library that provides you with modules to plot different types of graphs.
If you’re a newbie interested in deep learning, there’s yet another Python tool that is a strong fit for the earliest stage of your ML project. It’s Keras, a Python library used for the computation of running the neural network. Keras has a modular and minimalist interface built with a view to solve machine learning problems. It’s a solid and highly efficient library to start your deep learning project.
Finally, beginners may also find it useful to get the gist of how Scikit-learn works, as it has its own learning models and class functions. Its structure is often revisited in more advanced libraries and it shares a lot of solutions with other applications, which makes it a good introduction to using more complex resources.
Getting an idea of how Scikit-learn works is obviously just the beginning—the further you go, the more specific problems will stand in your way. Nevertheless, “Scikit-learn, as well as Pandas and NumPy, serve as a good starter pack for the so-called classic ML,” Łukasz says.
“Classic ML is the one based on thorough research and well-known methods that give you an idea of what to expect and offer you explanations on why something works or doesn’t. This makes that kind of ML different from the realm of deep learning, where we don’t have that many assurances as to what is going to happen,” Łukasz adds.
Engineering managers and tech leads are usually no strangers to figuring things out on their own. Is applying Python for ML purposes one of those things they could also learn without any assistance?
Here, it’s all about being familiar with data science and applying an engineering approach. Prior experience with Python will definitely help you, since you will already know where to look for the answers to the questions you may have.
What may bring you a new challenge, though, is the fact that ML stems from the academic approach, which may require using resources that didn’t make the top of your reading list before. If you want to get educated on the subject, theoretical publications may prove more useful to you than purely practical courses.
Python seems to be a ubiquitous tool of choice in more and more domains. The language is praised by various groups of tech whizzes, from game developers through data engineers to software developers.
Easy to learn and use, Python comes in handy for software developers who want to gain more flexibility while working on an ML project. Access to tens of open-source libraries and resources made by other Python users is a dream come true for any ML engineer.
What’s more, those facing the task of choosing the best programming language for their ML team will appreciate Python for its syntactic simplicity that facilitates cooperation between developers.
Having in mind its readability, stability, and integrability, using Python for ML is the smartest move to make. If you’re looking for an ML project technology that will bring together all the tools and solutions you need, as well as access to extensive documentation and the ever-growing community ready to help you—Python will address all these needs.
Thank you for reading our article. We hope it helped you wrap your head around the issue of using Python for machine learning purposes. Even though Python is currently the only choice for machine learning, thankfully it’s also a really good one.
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