We have come quite far in terms of technology. Looking at the timeline of history, it’s not quite long when we came up with the idea of wheels.
Soon the human curiosity led to the development of science and technology which further led the development of several technological marvels. These advancements have aided the growth of human civilization.
The discovery of any new technology has immense impact on civilization, so much so that it defines the era of discovery. Advancements in scientific knowledge and technological discovery have always happened in steps.
Everything, from the elaborate discovery of metals, the industrial revolution, and the development of machines, electricity, and computer science and technology has happened within 10000 years which is remarkable.
All the discoveries have had significant impacts during their times, and similar is the case with computer science and programming which is prevalent these days. Computers are present everywhere and programs permeate nearly every industry.
Apart from following the basic instructions, programs can be designed to learn from past experiences, which necessarily makes them Artificially intelligent, and the field is referred to as Machine Learning.
What is Machine Learning?
Machine Learning is a programming field where algorithms are developed which improve with every iteration.
It is relatable to a human learning process where we learn from our past experiences. This allows programs to make efficient decisions using reliable data.
Machine Learning is being used in several applications relating to Finance, Marketing, Recreation, Security, etc.
While several programming languages can be used to program a Machine Learning Algorithm such as Prolog, Java, C++, and R, Python is the first choice of the programmers in this respect.
Before we discuss the reason behind Python being the first choice, let us briefly introduce Python.
Python is a general-purpose programming language. Python is designed to make it conveniently readable and write programs with. Its Object-oriented approach makes it possible for programmers to write efficient codes.
Python was first released in the 1980s and has become hugely popular among the programmers.
Reasons To Choose Python For AI & Machine Learning
Programming is a time-consuming affair, so it is quite helpful to have some code handy. Python has a vast repository of code libraries and frameworks which provide code snippets for basic functionalities.
Specifically, for Machine Learning, there are extensive libraries that can save some time for the programmers.
TensorFlow, Apache Spark, and CNK are some frameworks that are used by Machine Learning professionals with computational libraries such as NumPy, SciPy, Panda, and others.
These libraries are meant for extensive statistical calculations and algorithm handling which plays an important role in Machine Learning.
*Easy to Learn
The popularity of a language is decided by the ease with which a person can learn the language. It also affects the feasibility of scaling the projects that depend upon that language.
As we described earlier, Python is a fairly simple language with normal, easy to remember and comprehend English syntax. Python programs are neatly structured with proper indentations and control flow.
Because of all these reasons, it is easy to learn and implement, making it a popular choice among Machine Learning Enthusiasts.
Python offers its users several choices on how to structure the program and makes execution of the program more convenient.
Python allows users to program their algorithms in either way- be it Object Oriented way, or by general scripting.
In Object-oriented programming, coding is done by declaring classes and using objects, on the other hand in scripting, classes, and functions are declared and used in a step by step method.
Moreover, Python can be easily used in combination with other programming languages. These make Python a very flexible language.
People work in different operating systems in different work environments. This can be a little problematic when collaborations are to take place.
Different operating systems have their structure, and at times programming languages are very differently written over different operating systems. However, this is not the case with Python.
Python is versatile as far as operating systems are concerned. It can be run on Windows, macOS, Linux, Unix, and other operating systems.
However, to run code on a different operating system based on Python, some small changes need to be implemented. To avoid these inconveniences, the PyInstaller bundle can be used.
Codes are often perceived as some dangerous technical gibberish by common people. Something that should be left alone with the professionals and not to be tampered with.
For such people, it is quite futile to try understanding some lines of codes. The design of Python, however, makes it quite comprehensible.
Python has simple English syntax, where the desired functionality is carried out using linguistically suitable keywords, methods, or properties.
Such a feature not only makes it easily learnable for the programmers but also easily readable. It makes errors and confusions minimal and facilitates efficient collaboration between teams and colleagues.
Python is a general-purpose programming language, and the resourceful libraries make it possible for Python to take care of a lot of coding requirements.
Like we discussed before, it has libraries that help it to handle data algorithms and perform complex calculations with ease.
However, for a data scientist, or an Artificial Intelligence developer, it is important to make the program visually representable.
To make the program visually representable, developers can use Python libraries such as Matplotlib that helps data scientists, or any programmer.
In general, to create graph plots, histograms, and charts so that the program output becomes comprehensible and the presentation becomes appealing.
Python is an open-source programming language, and an open-source product thrives on the support it gets. A strong community aids the product, aids itself, and the newcomers.
Since the day of introduction, Python has grown considerably, it has received several updates and the community has grown multifold.
For an aspiring developer, there are lots of resources available regarding Python online, the credit for the development of most of these resources goes to the community.
Today several libraries are utilized by programmers to develop efficient programs and the credit goes to the community.
Community members help each other resolve their issues and realize their goals, and most of them are free.
Python is one of the most popular programming languages in the world.
In the latest RedMonk Programming language ranking by MongoDB, Python has been ranked 2nd along with Java in the popularity rankings. Python is quite versatile, and big firms like Google, Instagram, etc. rely heavily on Python.
Python is being used for several programming purposes: Game development, Data Science, Web development, and so on.
The versatility and popularity make Python grow, allow collaborations, and provide opportunities and resources.
Artificial Intelligence and Machine Learning, apart from making lives comfortable, have given people a new perspective about the future.
AI has found its application in a lot of fields, and the rate at which new developments are being done, soon we are going to see AI assisting us everywhere.
With the advantages that Python provides and the collaborative efforts of passionate programmers, the possibilities are endless.