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Top 5 programming languages and their libraries for Machine Learning

In this data-driven era, Artificial Intelligence and Machine Learning are combined in every business. Machine learning-based solutions are used to increase the speed. There are many programming languages available for machine learning. Before learning about all the programming languages, let's learn about Machine learning. 

What is Machine Learning?

Machine learning is the branch of Computer Science and Artificial Intelligence whose primary work is to learn human results using data and algorithms. Machine Learning focuses on pattern recognition and data mining. It has three types of machine learning algorithms; these are as follows:

  1. Supervised machine learning algorithm.
  2. Unsupervised machine learning algorithm.
  3. Reinforcement machine learning algorithm.

There are some areas where Machine Learning is used. These are as follows:

  • Self-driving car.
  • Speech recognition.
  • Social media analysis.
  • Fraud detection.
  • Product recommendation.

There are several companies which use Machine Learning technology. These companies are Google, Facebook, Twitter, Apple, Salesforce etc. There are many programming languages around the world which are using machine learning technology. The best programming language for Machine learning and their libraries are explained below.

1. Python

Python is a versatile, lightweight, simple programming language which has the power to create web apps by using the framework. It was developed in 1991 as a general purpose programming language. Python has many helpful core libraries: Matplotlib, Numpy, Seaborn, sci-kit learns, etc.

  • Matplotlib- It is a popular library used to create graphs like lines, bar charts and many more.
  • Numpy- Numpy is a linear algebra library. It has a robust data structure. It is also called numeric python.
  • Seaborn- It can create a high-level graph.
  • Sci-kit learns- It is used in data analysis and data mining. It has machine learning algorithms like regression, and classification that supports gradient boosting, random forest etc.

2. Java

Java is a multi-purpose programming language. It is an Object Oriented Programming Language. Many high-profile projects are based on Java. Java has a robust framework like Weka and Rapid miner. The libraries of Java are as follows:

  • Weka- It is a portable library used for data analysis and mining. It supports data mining, data pre-processing, clustering, classification, feature selection etc.
  • JavaML- It is a simple and easy interface to implement the collection of data mining algorithms.
  • Deeplearning4j- It is an open-source distributed library that provides a framework for the machine learning algorithm. It helps identify patterns, sounds and text.
  • ELKI- It is an open-source data mining framework mainly focused on evaluating data mining algorithms.

3. C++

C++ is also used in the field of machine learning. It is considered a low-level language, so it is easily readable by the machine. It is very fast in execution, and its delivery speed is very high. The libraries of C++ are as follows.

  • TensorFlow- It is an open-source library used for numerical calculation on any CPU using a data flow graph.
  • Torch- It is also an open-source library which makes numerical operations easier by providing a large algorithm. It is used to improve efficiency and speed. 
  • mlpack- It is a flexible machine learning algorithm integrated into a large-scale machine learning solution.

4. R

R is a popular open-source data-driven language focused on machine learning environments. It has many salient features used for developing machine learning apps. It is also known for machine learning methodologies like regression, classification, tree formation and decision. The libraries of R are as follows:

  • xgboost- It is used for gradient boosting framework. It is famous for its performance and speed. 
  • Mar- It is the framework for regression and classification and has an extension mechanism through inheritance.
  • PARTY- It is used to create a decision tree based on a conditional inference algorithm. This package reduces the training time and bias.
  • CARET-  It is developed to prediction for different algorithms for a given problem and help them to choose which algorithm is best.

5. JavaScript

JavaScript is the most popular and high-level programming language. It is very flexible in nature. It is so prevalent in Machine learning. The libraries of JavaScript are as follows.

  • Brain.js-  It is easy to integrate with JavaScript, which is used in Node.js. There is no need for the neural network to work with this.
  • Tensorflow.js- By the help of this library, we can directly build and train the model in JavaScript.
  • Machinelearn.js- It is the replacement of the python sci-kit learns library.

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