Machine Learning with Python

...why learning the language will change your perspective

When it comes to learning a new skill, picking the one which is driving the cutting edge of today’s technology seems like a no-brainer. With an intuitive syntax, extensive Open Source support (and a name referencing classic British comedy), Python is the language known as the Swiss Army Knife of programming for a reason.

Which of these bits of code is easier to understand?

public class HelloWorld {
    public static void main(string[] args) {
        System.out.println(“Hello, World);
    }
}

Or

print(“Hello World”)

For those of us who don’t come from a background of computer science, the phrase “Hello World” may sound like an overly ambitious greeting after a few too many cups of coffee. However, when learning a new programming language, it is the first phrase you will produce yourself.

In the example above, the first bit of code is written in Java, perhaps the most popular (or at least most ubiquitous) programming language in the world. The second is written in Python, the language used in everything from Machine Learning (ML) to Game Design. As well as being a mature, field-tested platform, it’s also a language adopted by those who are coders on the side.

Code readability is the cornerstone of Python’s widespread adoption. Its syntax, designed around clarity, can be intuitively applied by those without an extensive software development background. Aiding in the quick implementation of new ideas, unhindered by complications created by more complicated styles of syntax.

With such large levels of usage from those who lack formal coding backgrounds, collaboration and experimentation within the Python framework have cemented its reputation as the Swiss army knife of programming. Its versatility stems from the huge amount of Python libraries, ready-made coding toolkits, for various applications. Whether that is Financial Risk Calculations, Astronomy Analysis, or Data Visualisation. There are over 133,000 of these toolkits on the Python Package Index alone, ensuring that users don’t need to constantly create new code.

For the tech companies of today, Python’s clear syntax and established reputation for versatility means that it is can be applied to any number of programming tasks. Web Scraping, Automation, GUI Development, and advanced Data Analytics are just a few examples of what companies use Python for.

For Google, the idea of “Python where we can, C++ where we must” became the central design philosophy thanks to Python’s ability to rapidly deploy, and maintain, programs.

Dropbox, a company with more than 500 million registered users uploading 1 billion files a day, has 99.9% of their code written in Python.

Within Facebook, Python is the third most popular language, providing 21% of their codebase infrastructure. Not only does Facebook implement Python in a similar way to Google, but Python is used to power their in-house Job Engine, File Distribution, and Workflow services.

With such active and high-profile contributors spread across a wide spectrum of use cases, it is no coincidence that Python underpins academic, research, and scientific communities - in turn driving AI, Deep Learning, and ML ever forward.

With such rapid development being made in the field, the unavoidable hum of companies offering services such as ‘Tailor Made AI Solutions’, ‘Custom Deep Learning Solutions’, and ‘Bespoke Machine Learning’ has quickly followed. While it is tempting to implement the latest technology in the search for "El Dorado", misconceptions surrounding it can result in complications, disappointment, and wasted opportunities.Self-aware machines are (sadly) still in the realms of science fiction, but advanced data analytics can be used at any scale.

Deep Learning and ML are the cutting edges of today’s data analytics. Having a proficiency in Python allows the full utilisation of Open Source tools developed for those seeking to harness the advantages that ML brings.

TensorFlow, developed by those behind Google Brain for deep neural network and ML research, chose Python as its first fully supported language precisely because of the large community of data scientists and ML experts who use the language.

Scikit-learn provides simple and efficient tools for applications ranging from image recognition to drug response prediction

For those wishing to take their understanding of ML to the next level, familiarity with Python is invaluable. Scientific, academic, and research communities all make extensive use of Python because of its clear syntax, extensive package library, and rapid deployment. Those who use grasp the foundations and importance of Python today, will find themselves in a better position to benefit from the future it is bringing.

By James Barry