In this article, we are discussing the Best Programming Language for Beginners in Machine Learning. In the world of Data science, machine learning technology plays a key role. You must aware that Machine learning is part of Artificial Intelligence. Specifically, it focuses on the learning of computation, mathematical algorithms, and data sets to decide without manual code.
Even though 5 programming languages perform well for ML projects, Python is the best one.
Let’s have a talk about Python.
Python helps ML developers and data scientists a lot in a faster and excellent development. Especially, the beginners get more advantages from Python to develop a successful machine learning project. Also, its an OOP based programming language. Can you believe that you can develop an ML project without coding through Python?
Here, you can use map, lambdas, reduce (foldl/foldr sorts, regarding Haskell). Also, go through libraries like scikit-learn, TensorFlow, Shogun, and swig magic API) to code for machine learning. The SHOGUN machine learning toolbox is much helpful for the developers to succeed.
C/C++ for machine learning
Move to C++ version 11 or 14 which helps in a faster calculation. Also, use Eigen3, Tensorflow, LAPACK, and ViennaCL for the C++ program.
Generally, we implement TensorFlow tool mechanisms for the development of a data graph. The API will be simple and brief for a graph operation. Graph operations neatly express through a functional style. It includes device placement, names, and many more. The resulting graph can run efficiently and the required outputs fetch in a few lines of code.
C# with Machine Learning
Anders Hejlsberg of Microsoft had launched C# in 2000. C# is a simple, flexible, advanced, secure, open-source, and object-oriented programming language. C# supports different kinds of applications including consoles, windows apps, mobile apps, and web apps, etc.
Also, C# helps us develop ML applications through a .NET core platform. Hence, we call this programming platform as ML.NET. This ML.NET is a cross-platform open-source framework that accesses machine learning for .NET developers.
This framework includes learning algorithms, ML data structures, and transforms. TensorFlowSharp is an open-source library that enables an API to work with the TensorFlow library through .NET, C#, and F#.
R with Machine Learning
R programming language is open source, dynamic, object-oriented, array-based, functional, imperative, and reflective to work with. In 1993, Robert Gentleman and Ross Ihaka created this programming language with name R. Initially, it didnt have that popularity. Data scientists, statistical engineers, and ML developers have been using it for the last few years. Its much popular for a statistical algorithm and functional features. Rs latest effective version 3.5.2 is much helpful. It supports various OS like Windows, Linux, and OS X. With TensorFlow, it allows you to work effectively through the high-level Estimator APIs and Keras.
Also, Develop Machine learning programs through other languages, like Julia, Shell, Go, Prolog, Ada, TypeScript, Lisp and Scala, etc.
DNN Frameworks and Libraries
Scikit-learn is an open-source framework library that supports Python for machine learning. Caffe supports Python and Matlab while Tensorflow supports Python and R. Numpy supports multinational data with mathematical operations.
Scipy framework contains pandas, scientific computation, visualization library, matplotlib, and dataset management, and a visualization library. Its valuable for ML and data science projects.
Also, costing is a significant factor in the implementation of any framework. So, you have to choose the framework accordingly.
In sum, we discussed the Best Programming Language for Beginners in Machine Learning. Readers must have a better idea about machine learning and data science projects. Especially, the beginners can go with Python or R for their Data science and ML project purpose. Building a career in Machine learning is really successful with Python. As the beginners can start ML projects with some readymade components without codes. Hence, depending on technical experience and requirements, the developers can incorporate the right technology. Also, costing and client preference makes the final decision.
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