What is Machine learning?

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Introduction:

The future of machine learning looks very bright because of the sudden growth and development in software & technology industry. Machine learning is currently one of the fastest emerging technologies in the world and many experts claim that it holds the key to unlock artificial intelligence. Machine learning is a powerful tool that is already being used to solve complex classification problems.

But as machine learning systems continue to evolve, there will be an increased demand for smarter languages that will be able to process a number of complex issues and general paradigms, some of which might be too complicated for humans to process. As the industry is growing with the experience of smart machine learning systems, the entire field of machine learning is being staged to shift from simple problem solving to the creation of powerful and complex algorithms that work on advanced-level. A number of machine learning languages have already paved the way for the future of integration and artificial intelligence.

What is Machine Learning?

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.

Let’s take a look at the top machine learning languages:

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R language

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R is a really powerful language to start with machine learning, as there are many specified GNU packages available. One can surely choose to use R for creating powerful algorithms and the R studio has an easy statistical visualization of your algorithms. The R machine learning language is meant for the advanced user because of its complex nature and wide learning curve. It is the perfect platform for those looking to comprehend and explore statistical data via a graph. It is a competent language for those who want to discover, design and test algorithms. It is best suited for one-off projects that comprise of artefacts, such as reports, research papers, or even predictions. R is currently the single most popular machine learning platform for competitors, like Kaggle, as they prefer it over other languages like Python.

 

Python

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Python language is one of the most flexible languages and can be used for various purposes. Python has gained huge popularity base because of this. It has become the machine language of choice for developers who are looking to frame better questions or expand the capabilities of their existing machine learning systems. Python is a comprehensive language that covers a range of libraries, including those of Teano, Keras and scikit-learn. It also features easy to comprehend walkthroughs and even useful tips from opinion analysis to neural networks, allowing users to find answers to complicated issues. The only drawback of Python is that it is relatively more fragmented than other machine languages.  For beginners, this is the best language to use and to start with.

Java-family/C-family

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The mother of all language C is definitely a great programming language to build predictive algorithms. Machine learning is a sequence of complex algorithms and the C-family machine learning language is the perfect example of how good design and user-centric features can automate sequences. For consequential production implementations, C offers users a robust library that allows them to customize implementations of project-specific algorithms.

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The Java/C-family machine learning language is a sanctuary for the seasoned developer who has the time to make minor tweaks using comprehensive libraries. Most of the current and older machine learning algorithms are written in Java. Even deep learning implementations for LIBLINEAR and LIBSVM are written in C-family language. Java is a functional programming language that will allow future machine learning systems with speed, accuracy, and precision.

Conclusion:

There are many other programming languages that you can use after going through the above languages.  But as a beginner, it is better to start with Python and move to other languages once you get the command of that.