As tech industry sectors like machine learning and data science have grown in recent years, so has the need for a stable, versatile programming language to aid in research. Python has been around for over 30 years, but it’s actually become a favorite amongst those industries for research purposes.
As an example, there’s been a lot of buzz lately around deepfake apps that can realistically morph your face onto a celebrities body. These apps usually have pretty humorous results, but the ones powered by AI are trained on image recognition, typically written in Python.
That’s just one example of how Python accelerates research in machine learning, and there are plenty others. Let’s talk more about why Python specifically has been chosen as a favorite programming language for these purposes.
It’s versatile and easy to learn
Python is a very versatile programming language that can be used in AI and machine learning, web development, Python web scraping, business applications, data analysis, game development, and more. It’s wide range of applications has led to it having great support, with over 125,000 third-party libraries available.
There’s also a lot of online courses available for learning Python, which come in all formats, so you can take part whether you’re a DIY learner or prefer a more virtual classroom environment. You can easily find solutions and community support from any number of programming and development communities, such as Reddit, PySlackers, and Discord channels.
Python isn’t as complex or difficult as other popular languages like C# or Java. In fact, many say that Python is easier to read and understand. This is because Python’s syntax is very concise and almost human-like, with an emphasis on natural-feeling language while writing code.
Because Python is an object-oriented programming language with dynamic semantics, it’s ease of use allows more increased productivity. For example, there’s no need to compile your code for execution, as Python can be processed in real-time by the interpreter.
Python can be integrated with C languages
One of the downsides of Python is that it may run just a little slower than other programming languages, by itself. This is because Python is an interpreted language, rather than a compiled language. Interpreted code will be slower than compiled code because it requires more instructions to run the interpreted code compared to direct machine code.
Fortunately, you can easily implement C/C++ into Python, giving you the ease of coding with Python, and the speed of running compiled code with C languages. An extension like Cython can generate C code from Cython code. Cython as a language is basically Python, with additional optional C-language syntax. It’s really useful for just squeezing a bit of extra speed performance out of your Python code when you need it.
Access to cloud-based Python libraries for machine learning and data science
Cloud-based services like Jupyter Notebook, or a fully-fledged IDE like PyCharm, can hook you into Python environments like Google Colab, where a lot of libraries containing live code, equations, and more are available. These can be used for machine learning, data science, and research, and setting up machine learning models, even on mobile phones.
In other words, you have access to a lot of data that will help you get set up and running much faster, allowing you to put more focus into end-results.
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