Machine Learning Made More Effective Through Python

Machine learning, a branch of artificial intelligence, is a method of data analysis that automates analytical model building. While artificial intelligence covers the broad concept that machines should be able to perform what humans consider “intelligent” tasks, machine learning is based on the idea that machines should be able to learn and adapt through experience.

The introduction of new computing technologies has evolved our understanding of machine learning since its inception. One of these technologies, Python, has emerged as a clear frontrunner as far as a machine learning language. Many data scientists and developers have agreed that Python has made machine learning faster and simpler than ever before, giving it a noticeable advantage over its competitors.

How Machine Learning Has Evolved

Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important, because as models are exposed to new data, they are able to independently adapt. These models learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

  • Ever wonder how Google’s self-driving car can compute and navigate a route seamlessly? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? Perhaps one of the more obvious, important uses in our world today.

Machine Learning Is Essential

The rising interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Breakthroughs such as growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things means it’s possible to quickly and automatically produce machine learning models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. By building precise models, an organization has a better chance of identifying profitable opportunities while avoiding unknown risks.

As the role of machine learning increases in importance, so has the use of Python.

Using Python for Machine Learning

When analyzing the overall popularity of machine learning languages, few can stack up to Python. As a matter of fact, 57% of data scientists and machine learning developers use Python while 33% prioritize it for development¹. Can’t say we’re surprised, given the evolution in deep learning Python frameworks over the past two years – including the release of popular open-source software library TensorFlow, and others. These libraries help shorten the time between starting a project and doing meaningful work within that project. Below is a list of Python’s tools, which are free and designed to work well together:

PYTHONTOOLS

Additionally, Python is prioritized the most by those for whom data science is the first profession or field of study. This indicates that Python has by now become an integral part of data science , and  has evolved into the native language of data scientists. The same cannot be said for R, the language preferred by most data analysts and statisticians, as it was initially created for them and meant to replace S.

Python’s flexibility and easy-to-learn platform makes it a great choice for production use because when data analysis tasks need to be integrated with Web applications, you can continue to use Python instead of integrating with another language. Also, Python’s syntax is more similar to other languages than R’s syntax is. Python’s readability is nearly unmatched, as it reads much like a verbal language. This readability emphasizes development productivity, while R’s non-standard code could lead to stutters in the programming process.

These are a few of the many examples of why Python beats out its competitors. Granted, R is a great data analysis tool, but is fairly limited in terms of what it can accomplish beyond data analysis. When considering a language for machine learning, Python has proven effectiveness.

Endnotes

1. Medium – “What is the best programming language for machine learning?”

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