- Machine Learning and its significance in various sectors
- Popular sites/apps built in Python
- Advantages of using Python in Machine Learning
- Machine Learning projects/solutions in Python
Whereas some programming languages have been developed and stagnated for quite some time, others have shown quick growth and popularity among programmers. Statistics from Stack Overflow show that Python is the programming language with the fastest growth rate currently. Various factors have influenced its growth and include the following;
First, programmers find it easy to use Python to fulfill a wide range of purposes. Web developers find it convenient and less complicated to use. The case is the same for developers of mobile applications as well as those focusing on data science. Further on, in a world where money runs everything, Python becomes the top priority language for many programmers and students, but why and how? A huge pool of both current and future programmers and students are interested in knowing and learning about what will land them in well-paying jobs. Python being on high demand, therefore, becomes an ideal language to venture into.
The versatility of Python is another factor that makes it a global trend. Being a multi-purpose language that is applicable in a vast range of tasks such as web and app development, its overall growth can be examined by monitoring the levels of traffic growth of popular packages developed using Python. Stack Overflow, for example, highlights Pandas to be the fastest growing package from Python. Developed in 2011, Pandas is attributed to 1% of the total views of Stack Overflow questions.
Machine Learning and its significance in various sectors
Machine Learning (ML) is also a large contributor to the growth of Python. Machine Learning is described as the application of data while teaching a machine how to make decisions that are accurate. There exists no specific programming language dedicated to Machine Learning. By, however, looking at the distinct characteristics of individual languages that can-do machine learning, developers can easily choose a language that best fits their desires. Python has become a common choice for many ML engineers due to its ease of use and compatibility. It is specifically highlighted for its ease when developing intelligent algorithms from vast, unstructured and incomplete data.
Various reasons render Machine Learning important in different sectors. First, producers of self-driving cars could have never achieved their dreams with the exemption of machine learning that enables algorithms to interpret and analyze 3D images.
Though most of these innovations are past the testing phase, self-driving cars taking to the road on their own will be a sincere proof that machine learning will have enabled them to respond to the surrounding situations in real time. Rather than submitting information acquired through sensors to a cloud or a server for analysis, the car is able to process such information and derive a suitable action rapidly.
Other areas where ML is significant are manufacturing industries where large production plants can perform most of the operations on their own when properly programmed. Think of finance where systems alike Automatic Teller Machines are able to analyze a client’s data and perform a series of commands. Online customer support, virtual personal assistants and commuting predictions are other sectors where ML plays significant roles.
Popular sites/apps built in Python
With all the aforementioned significance and popularity of Python, tangible evidence marked by its role in the development of famous applications exist. There are various popular applications and websites whose foundations lay on Python. YouTube applied Python to facilitate the integration of video streaming, embedding of certain information and the ability to like videos in their pages.
Google, a giant search engine operates its mainframe foundation using Python. Various applications that are run by Google in concurrence with the main site are also developed using python. Quora, famous for its perfect answers to questions from a vast range of topics use Python language. Quora was designed and created by former employees of Facebook who used the Python language to create the globe's paramount magic eight balls in Quora.
All online marketers must have thought about marketing their products on Instagram. Thanks to Python for bringing into existence such a helpful app that enables multiple users to post, browse and find pictures of their choice in the site. Reddit is among the monster open communities found in the web where specific questions, specific talks or much information relating to a particular topic can be found. This site uses Python language to store usernames, create categories of subreddits, and gold was awarded to posters that are highly valued.
Advantages of using Python in Machine Learning
Developers of machine learning derive many benefits when using Python as their programming language. Below is a comprehensive list
- It is fast to develop, easy to use and versatile.
- It is an open source engulfed in a vibrant community
- It is convenient for prototypes where more can be achieved with less coding
- It was comprised of a comprehensive set of extensive libraries
- It is possible to extend it to other languages
- It is embeddable, an attribute that complements its extensibility. It is, therefore, possible to put Python code in a source code of a different language like C++.
- It is object-oriented and thus facilitates input of specific commands to give specific results in machine learning
- Has higher productivity compared to other programming languages? It can, therefore, be used to develop more than one command in ML.
Machine Learning projects/solutions in Python
There are various Machine Learning projects and solutions that have been developed using Python language. TensorFlow which was initially developed by Google Brain Team is one of them. This system facilitates ML research and makes it easy and quick to switch to the production system from a research prototype. Scikit-Learn is a solution that makes data analysis and data mining accessible to everyone. Built on a commercially usable- BSD license, this solution is reusable in a variety of contexts.
Theano is a Python-based project that enables users to define, optimize and evaluate multi-dimensional arrays of mathematical expressions effectively. It is very helpful for large financial institutions. Chainer is an open source and standalone Python-based framework used for deep learning models. It is notable for facilitating implementation of several state-of-the-art models such as variational auto-encoders through intuitive, flexible and high-performance means.
As such, it can be concluded that Python programming language is a huge benefit to programmers. Its ease of use and convenience makes it the top choice among various users of programming languages. In the business world, Python has facilitated enormous growth through automation of various processes. Business owners are able to easily track business dealings with the use of various programs developed using this language.