Python has become the world’s most widely used language according to the IEEE Spectrum for programming languages 2019. Thanks to researchers around the globe, knowing facts like these is possible and helps us deduce essential conclusions.
Python was successful in beating the world’s once most popular language JAVA near the end of 2018. The two had a fierce battle with 7 million Python users and 7.1 million JAVA users last September. But, the question is what led to the sudden rise and popularity of Python.
There are no doubts that a large number of full-stack developers have moved to Python due to its user-friendly code. It is, however, only machine learning that is giving it the wings to fly. The realization of Machine Learning in the world today has opened more than a few opportunities in all walks of life. And when it comes to the tools necessary to build it, Python developers India tops the charts.
Python is people’s preferred language for implementing machine learning for many reasons. Be it the minimalistic syntax, an abundance of libraries or ease of execution. Technically speaking, Python is the Bayes classifier of machine learning.
Decoding the Bayes Classifier Analogy
Those familiar with the mathematics of machine learning know why a Bayes classifier is often called as an optimal classifier. For those who are new to classifiers, they are some measurable functions that determine the probability of any class of having a specific label or not. Classifiers are an implication of the supervised learning approach where the computer program learns from a given data and uses this learning to classify new observations.
For example, a machine learning classifier helps in recognizing speech, handwriting, bio-metric, documents etc. The Bayes classifier is one of the simplest classification algorithms that can be extended to find almost all classifiers in machine learning. It wouldn’t be incorrect to say that the Bayes classifier is the underlying mathematical foundation of vast machine learning algorithms.
Another reason why Bayes turns out to be an optimal classifier is due to its risk minimization properties. Whenever we design a machine learning algorithm, the prime focus is to minimize the risk. It is what helps in getting accurate results.
Also Read: Performance Optimization Tips for Python
The Bayes classifier is fundamentally related and derived from the Bayes rule of the probability theory. Even mathematically, it is easy to show why Bayes classifier is an optimal classifier and helps in minimizing the risk. No matter which algorithm one tries to develop, they can find its foundation stone lying in the Bayes theory of classifiers. This theory resonates with the concept of Python being the quintessential choice for machine learning.
Sharing similarity to a Bayes classifier, Python stands firmly in implementing all machine learning algorithms. It has a plethora of libraries that are just built for machine learning. While these help in computing complex mathematical calculations, there are robust functions and classes defined for different kind of error predictions, model structures among others.
Lets’ take a look at some of the other reasons why Python is the Bayes classifier of machine learning–
Works Well with Different Kinds of Data
One of the best qualities of Python is that it works like a dream no matter which kind of data you provide it. Be it images, texts, videos etc. Python can help you implement machine learning algorithms on all of them. While it may seem like an organic task, it is much rigorous in practice. Raw data is usually unstructured, incomplete and large, among other things. But, Python via its multiple libraries enables a user to practice machine learning with ease.
Mathematically strong packages
Some of Python’s packages like Numpy are advance mathematics-based libraries, fundamentally designed for machine learning. If you want to work with images, use Numpy, Scikit, Open CV etc. When working with deep learning, use tensor flow, Pytorch. Similarly, if you want to see your data use visualization packages such as Matplotlib, seaborn and more. The point is, whatever you want to accomplish, Python has a robust package in its store.
Flexibility
Python for machine learning offers a great deal of flexibility. It provides a choice to either choose the object-oriented concept of it or merely scripting. There is also no need to compile the source code again and again. Developers can implement any changes and watch the results instantly.
Platform Independence
Versatility is one of the characteristics of Python. It means that Python for machine learning can run on multiple platforms such as Windows, Linux, macOS, Unix, among others. With packages like PyInstaller and a few small scale changes, one can prepare their code for any platform.
Conclusion
Python’s in-built characteristics for machine learning simplifies the development process and cuts the development time. It helps programmers concentrate on maximizing the efficiency of their machine learning models instead of being tangled in large pieces of code. A Bayes classifier does similar things for machine learning.
