Do you ever notice how children can learn so well by imitating the adults or learning by experience? The human brain is pretty sophisticated, in the way it learns and the way it projects things. From the past hundred years, we have reached closer to creating brains of our kind. The progress has been pretty slow, but significant.


One of the most significant advancements we have made in the field of consumer technology over the past few years have been related to Internet of Things, API, Bitcoin, and Artificial Intelligence. In fact, artificial intelligence is pretty much considered the future of most consumer-driven tech markets. Apple has Siri, Amazon is bidding high with Alexa, Google has the Google Assistant, so on and so forth. It was only last year that the first Robot-citizen of the world, Sophia, was interviewed by a journalist!


What is setting these artificially intelligent bots apart from the class of bots we used to have earlier is their ability to make sense of the world and learn a few things on their own, by collecting data and making an intelligent conclusion out of it. They are constantly learning, bettering their programs and making more ‘humane’.

Machine Learning: What Is It All About?

Machine learning is a stream of artificial intelligence which focuses on making robots that can, in essence, learn. This requires a complex understanding of some programming languages and being able to develop machine learning algorithms. Psychology and mathematics also are secondary subjects a developer needs to understand how to build a machine learning algorithm.

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Which Language Is Popularly Used For Machine Learning?

There are surveys and data at Developer Economics which point out that Python is the most popularly used language for development. With over 57% developers choosing and 33% prioritizing it, Python has surpasses the other alternatives like R or Java. One attribute that is most preferred about Python is the flexibility of the language and the constant innovations being done to keep it relevant. With special libraries for Machine Learning, and the release of TensorFlow, it is a language that helps beginners learn kernel methods and is great for linear algebra.

However, let us understand that there are widely different areas where one needs to develop machine learning algorithms. The same study shows that when it comes to sentiment analysis, data scientists and developers prefer Python. While on the network security front, Java was preferred and prioritized.


R is another preferred language when it comes to visualizing how to build a machine learning algorithm. It is a modern, more advanced form of the S language, built in Bell labs. Many professionals feel that R gives a competitive advantage, siting that it has more to offer in terms of data analysis and algorithm selection.


However, Python still remains a very popular language to get your hands dirty with. With predictive analysis and machine learning, this language has become common with the employers and it is a much sorted after skill for a developer who desires to work in the field of machine learning or artificial intelligence in general.


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