Which Is the Programming Language Not Suitable for Machine Learning?

R

R is not suitable for Machine Learning because it is a statistical programming language. What makes R attractive to data scientists is that it has a very powerful set of libraries and functions for statistical data analysis. This can be used to perform complex mathematical operations like fitting multi-variate curves, various kinds of regression models, calculating the correlation coefficient between two variables, etc. However, most of these operations are not applicable to ML tasks as they do not involve any kind of machine learning algorithms.

R also lacks several essential features needed for developing machine learning solutions such as optimization techniques (ex: gradient descent), neural networks (deep learning) and reinforcement learning algorithms. Most importantly, R does not have any built-in parallelization mechanism which makes it difficult to scale up your model training process if you want to use large amounts of data or run heavy computations on your computer using multiple CPU cores at once.#ENDWRITE

C Sharp

C# is a general-purpose language, which means it can be used for many different programming tasks. But C# doesn't have any built-in mechanisms for machine learning. To get started with machine learning in C# you'll need to use one or more of the following three approaches:

• Use an existing library that provides the necessary functionality (e.g., .NET). You may find this approach cumbersome because you have to learn how to use the library and its documentation before getting started with your project.

• Write code yourself using a collection of libraries and algorithms from different sources on GitHub that might not be well documented or tested (for example, see [this list](https://github.com/shamimkhan/ml-in-csharp) created by a researcher at Carnegie Mellon University). This approach allows you greater freedom but also requires extensive programming skill and familiarity with multiple libraries and algorithms.

• Use other languages such as Python or R which have been specifically designed around data science tasks including machine learning; these languages are easier than C# but require installing additional software before they can run locally on your PC (iPython comes preinstalled on our cloud servers).

Python

Python is a high-level programming language that has been around since 1991. It’s an open source language, which means it's free to use and modify by anyone.

Python is a popular choice for machine learning and AI because it offers several advantages over other languages:

• Python is easy to learn, thanks to its simple syntax and clear documentation.

• Python programs can be run on multiple platforms, so you don't need to worry about compatibility issues if you want your program on Linux or Windows.

• Python has extensive libraries for math processing, data analysis, text processing, web development and more!

Go lang

Go is a programming language developed at Google in 2007. It is used in some Google’s internal projects and has been gaining popularity as an alternative to languages like Python and C++.

In the recent release of TensorFlow it is possible to do machine learning using Go, so if your project requires heavy computation then you should consider using it.

Go is a compiled statically typed programming language with a syntax loosely derived from C.

All of the above.

None of the above.

Actually, as you can see from the table at the top of this section, there are several programming languages that are suitable for machine learning. However, it’s important to keep in mind that all programming languages have their strengths and weaknesses. For example:

• R is a functional programming language. It is not optimized for data manipulation, so it might not be the best choice if what you want is to quickly get your hands dirty with some machine learning techniques before you go any further down that rabbit hole (see Machine Learning Tools).

• C# is a multi-paradigm language whose syntax will never let you forget how much time has passed since 1999 (that's when Microsoft first introduced Visual Studio).