Home Cloud Computing Best 20 Machine Learning Language to Boost IT Career in 2020

Best 20 Machine Learning Language to Boost IT Career in 2020

Machine learning is the real-life application of artificial intelligence to learn and execute tasks automatically without the interference of human beings. It is the scientific study of various mathematical, statistical and geometrical models that organized through a set of algorithms. To implement those algorithms you need the knowledge of machine learning language. ML language will help you to develop your desired machine learning project. Moreover, those languages will boost your IT career to get a splendid job in the field of artificial intelligence.

Best Machine Learning Language


ML is a vast area where each part of ML architecture is done by different machine learning language. All of the ML languages are not applicable to you. But the purpose of today’s endeavor is to appraise the salient points of different languages for the assimilation of deep learning and artificial intelligence. You can complete your machine learning thesis project by any of the applicable languages. Moreover, I am not writing the serial based on any importance. Let’s have some idea of the Top 20 Best Machine Learning Language to Boost IT Career in 2020.

1. C# Machine Learning


C# Machine LearningC# is one of the best machine learning languages to develop any application in .NET framework. Microsoft created an ML.NET package that provides the basic functionality of machine learning. Moreover, it is easy to add various functions from C#. If you want to implement AI with machine learning then C# will be the best choice.

Various Projects With C#

  • Using C# you can develop a lot of machine learning project some of them are enumerated below:
  • You can develop a smart email filtering using.NET framework of a desktop application.
  • Integration with social media like Twitter sentiment analytics.
  • Credit card fraud detection can be one of the best ML project using C#.
  • If you want to develop a cyberattacks prevention tool you can make it with C#.
  • C# supports many forecasting projects like foreign exchange rate prediction.
  • C# has a contribution to business applications and customer segmentation.
Other Features of C#
  • Without the help of UI C#, Windows service long run the process.
  • It also develops the client-server and web applications.
  • The web applications support functionality over HTTP.
  • C# is also good for big data analysis.
  • Using ML.NET is much easier than another language.

More Info of C

2. Javascript Machine Learning


Javascript machine learningJavascript is a great programming language that represents in short JS. It is a high level and just in time compiled interpreted programming language to confirm ECMAScript specification. It supports HTML and CSS so implementation through a web application is easier. The library of JavaScript is high and quite easy to use.

Various Projects With javascript machine learning

  • You can integrate JavaScript with machine learning to make neural networks. The brain library helps to take input and provide output.
  • Deep Playground is another educational project 
  • The various web-based application can be used by JavaScript machine learning.
Other Features of javascript machine learning
  • Javascript has a rich library that can be used for making various machine learning projects. Some of them are:
  • TensorFlow.js: It is an open-source library that allows us to implements machine learning through the browser.
  • Machine learning tools: It is another high ML library that performs the mathematical, statistical and data processing tasks through supervised learning.
  • Keras.js: This framework offers GPU mode support using WebGL. It works as a backend framework, such as the Microsoft Cognitive Toolkit (CNTK).
  • Brain.js: It is specialized for designing the process and implementing neural networks.
  • STDLib: It is used for implementing a high-performing machine learning models and numerous machine learning projects.

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3. C++ Machine Learning


C++C++ is most of our first language of programming. After arising the concept of machine learning Python has taken the position of C++. But, you can code your machine learning language by C++. Moreover, many people use C++ instead of python because of its fastness. If you want to develop machine learning in a production system you can go for C++.

Various Projects With C++

  • Machine Learning Gladiator: It is an old concept to set a goal out of the box. This model is helpful for Importing data, Cleaning data, Splitting it into train/test or cross-validation sets, 
  • Pre-processing, Transformations and, Feature engineering.
  • Iris Flowers Classification
  • BigMart Sales Prediction
  • Sentiment Analysis using the Twitter Dataset
  • Movie Recommender System with the Movielens Dataset
Other Features of C++
  • C++ is fast and performance is very high.
  • Because of big mathematical operations, C++ provides a big library.
  • It is a cross-platform language that means you can write a program to any operating system.
  • C++ is good for upgrading the existing apps but if you want to develop new apps then go for any newer language.

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4. GoLang Machine Learning


GoLang Machine LearningGoogle has brought a new machine learning platform for ML developer widely known as GoLang. It is similar to c/C++ so all the library functions and calling procedures are the same. It is more efficient and easy to deploy than its competitors. GoLang is user-friendly so it facilitates evolving codebases, concurrent/distributed systems and Improves collaboration with developers. It has integration with other languages so you can call functions from other programming languages.

Various Projects With GoLang

GoLang is a product of Google. Much big organization has implemented this single application for developing their projects. Some of the projects with GoLang machine learning language are:

  • Docker: The potential big organization can implement the Docker project. It helps to work against vendor lock-in. It helps to expand the organization in the local environment. If you want to implement new technology you can use GoLang programing language to implement with Docker.
  • Etcd: It is an open-source distributed key-value store that written in GoLang. It provides a reliable way to store data across a cluster of the machine.
  • Kubernetes: it is another open-source application to manage containerized applications across multiple hosts and offers development process and maintain the application. 
  • Syncthing: This continuous file-sharing project is developed by GoLang machine learning language. It works on both Lan and the internet so it does not requires any advanced configuration.
  • InfluxDB: it is another time series program used for storing and querying data, processing.
Other Features of GoLang
  • GoLang is easy to learn like python. In some cases, it has crossed the Python in terms of easiness. Some coders believe that it is as simple as JavaScript.
  • The compilation capabilities of GoLang are quite good because along with its module it links all its dependency libraries into one binary file.
  • It is more resource-efficient and fast.
  • It has integration with Cloud IDE and debugging tools.

More Info

5. PHP Machine Learning


PHPIf you need a high-level machine learning and deep learning library you may use PHP programming language. This open-source and free programming language is developer-friendly that supports high-level API. PHP algorithm supports cross-validation, preprocessing aad neural network. PHP-ML is licensed under MIT license. It is a server-side scripting language that can develop various machine learning projects.

Various Projects With PHP

  • You can develop variously ML Project using PHP. Some of the projects are:
  • Calculating Census Income Data, managing Movie Lens Data, Trip History Data, Human Activity Recognition Data, and Wine Quality Data Machine Learning projects.
  • Image Recognition, Speech Recognition, Medical diagnosis, Statistical Arbitrage, and Learning associations machine learning project.
Other Features of PHP
  • PHP-ML can implement various machine learning models like Classification, Regression, Clustering, Metric, etc.
  • PHP also can be used for neural networks.
  • Many machine learning developers are well acquainted with the PHP prediction algorithm.
  • PHP also supports artificial intelligence in ML.

More Info

6. Octave Machine Learning


octave ML LanguageGNU Octave is a high-level programing language used for developing machine learning projects. It supports both linear and non-linear problems and very compatible with MATLAB. It helps to learn algorithms in a clear way. It is a great machine learning language for implementing prototyping and various experient.

Various Projects With Octave

  • Octave supports various mathematical models so you can prepare a project with structural data.
  • You can develop various simulation type projects.
  • Where prototyping is required you can implement Octave.
Other Features of Octave
  • Octave supports all the operating systems.
  • This program is very easy to use as any other programming language.
  • It is good for prototyping. 
  • It is a free alternative to MATLAB.

More Info

7. Julia Machine Learning


JuliaJulia is a high-level machine learning language that has some unique features to learn and utilize. If you are an ML developer then you will be excited with new programming language like Julia. The syntax of this language is similar to Python. It is highly effective for probability and statical analysis. Though it is not object-oriented it has some unique tools like predictive modeling, visualization and, machine learning.

Various Projects With Julia

  • Julia is used for astronomy projects.
  • It can be used for developing medical projects like Diabetic Retinopathy.
  • The project of a comprehensive analytics solution.
Other Features of Julia
  • The programming language Julia comes ready with Flux, a state-of-the-art framework.
  • This dynamic programming language can be used for computational science.
  • It is faster than MATLAB.
  • Julia is not OOP but offers many tools.

More Info

8. node js Machine Learning


node jsNode Js is a rich library of JavaScript. It is machine learning magic for your JavaScript application. Using your own data it retrains pre-existing ML models. With the help of flexible intuitive API it built and train model directly. 

Various Projects Node js

  • You can develop a real-time piano using a neural network.
  • You can make a webcam controller project to run on the browser.
  • A movie mirror project can be the interesting one using node js.
Other Features of node js
  • Node. js is better for Website development.
  • Node. js uses an event-driven and, non-blocking I/O model.
  • It is lightweight and efficient,
  • Node. js is perfect for data-intensive real-time applications.

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9. Scala Machine Learning


Scala Machine Learning LanguageScala is another machine learning language that is used for deep learning. The data scientists considered it a distinguished programming language in recent years. It is a compiled language where the program is faster than Python. To learn the programing language you need the knowledge of statistics and mathematics.

Various Projects With Scala

  • Regular & Harmonic Chart Patterns.
  • Confirm the pattern’s validity.
  • Learn How to Scan different Chart patterns.
  • create attractive & consistent profits in the Stock Market.
Other Features of Scala
  • Scala is a high-level language that compiles object-oriented programming and functionality.
  • It is a good choice for data science in implementing big data.
  • Python is easier than Scala. The importance of Scala is also more.

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10. Machine Learning with R


Machine Learning with RR is a free software environment that compels and runs a wide verity of Windows, Mac, and UNIX platforms. It is a GNU project that is used for developing the statistical and mathematical models. R is developed in Bell Laboratories by John Chambers and colleagues. Ris the implementation of S but not as easy as Python. To work on machine learning R users have to take pain at the beginning.

Machine Learning Project With R

R Machine learning language is used widely to develop ML Projects. It is good for an end to end data science and big data projects. R already covered a long way to proves its capability in real-world life. Some R projects are:

  • Music recommendation system using R ML language.
  • Wine quality prediction project using R.
  • You can prepare a statical model of credit default by R.
  • Forecasting inventory demand with historical data using R.
  • Coupon Purchase Prediction Machine Learning Project using R ML Language.
Important Features of R
  • Machine learning is based on clustering and finding predictive patterns where R is used to support the prediction.
  • If you want to use the k-Nearest Neighbors (KNN) machine learning model you can use R.
  • You will get different machine learning ebook using R.
  • R is rich with various libraries, functions variables, various plotting charts, and many other video tutorials.

More Info

11. Kotlin Machine Learning


kotlinKotlin is a general-purpose cross-platform and statistical type programming language that incorporates Java. it has caught the attention of the Android community by compiling down to Java bytecode. The main focus of Kotlin on interoperability, safety, clarity, and tooling support of machine learning. Because of the statical features, many java developers shifted and rewrite their Android apps with Kotlin.

Machine Learning Project With Kotlin

Many Machine Learning Project of Java and Python has shifted towards Kotlin because of may reasons. If you want to write 10000 line coding then it is better to choose Kotlin than Python. Some ML projects with Kotlin are:

  • Various projects of neural networks.
  • Clustering air quality data projects using Kotlin.
  • Machine learning projects based on Android applications are done by Kotlin.
Important Features of Kotlin
  • Kotlin is a supervised machine learning language.
  • It supports various types of algorithms and machine learning languages.
  • If you want to implement an artificial neural network and linear regression you can use this ML language.
  • Kotlin is good for training and testing.
  • In various aspects, Kotlin is best for the machine learning ecosystem.

More Info

12. Semi-Supervised Learning Python


Semi-Supervised Learning PythonPython is an interpreted, high-level and general-purpose programming language. If you need dynamically typed, garbage-collected and multiple programming paradigms you must use this procedural, object-oriented, and functional programming machine learning language. It is useable as an extension language that offers easy to use automation interface. Python is concise and readable that has versatile workflow to stand behind machine learning and artificial intelligence. It is the most used programming language for machine learning language. 

Machine Learning Project With Python

  • Amazon Product recommendations, LinkedIn, Facebook and even search engines are based on Python machine learning language.
  • If you need to collect data from the various channel you can make the project of Python.
  • If you want to develop any IoT Project then Python is one of the best machine learning languages.
  • You can do many other projects using Python for example:
  • Identifying Twits on Twitter
  • Finding the Frauds
  • Catching Crooks on the Hook
  • Uber Helpful Customer Support
  • Barbie With Brains
  • Netflix ‘n’ Change
Important Features
  • Python is a great object-oriented, interpreted, opensource and interactive programming language.
  • It supports various Cloud IDE.
  • The syntax of Python is clear and remarkable like C, C++, and JavaScript.
  • The Python is slower than many other programming languages.
  • It can integrate various third party language and platform.

More Info

13. Clojure Machine Learning


Clojure Machine LearningI want to introduce another machine learning language with FossGuru readers. The programming language Clojure is a general-purpose dynamic programming language that combines all scripting languages with the robust infrastructure for multithreaded programming. This powerful programming language supports a rich set of immutable, persistent data structures that offers a simple, coherent, and powerful tool. As a machine learning project developer, you can use this Clojure ML language.

Machine Learning Project With Clojure

You can develop various deep learning project using the Powerful programming language Clojure. Some of the probable projects of Clojure can be:

Important Feature
  • Clojure is designed to be hosted on JVM.
  • This system utilizes runtime polymorphism that is easy to change and extend.
  • It is a member of the Lisp family of languages.
  • The development of Clojure is dynamic. So data loaded, adding features, fixing bugs, testing will be easy.

More Info

14. NLP With Deep Learning


NLP with deep learningNational Language Processing (NLP) is an artificial intelligence that understands human speech as we speak. The purpose of this language is to get the job done by voice command. If you want to integrate machine learning with artificial intelligence you can use the NLP machine learning language.

Machine Learning Project With NLP

If you want to develop any system that understands human voice then you can make a project with NLP. You can develop customer support related projects using NPL. Some of the possible ML projects of NLP are:

  • Teach a Neural Network to Read Handwriting
  • Investigate Enron
  • Language detector
  • Write ML Algorithms from Scratch
  • Mine Social Media Sentiment
  • Predictive text generator
  • Media monitor
  • Improve Health Care and
  • Various projects of deep learning.
Important Feature
  • NPL uses a natural language processing algorithm.
  • You can integrate all the programming languages with NLP.
  • It has many other features like text planning, sentence planning, and text understanding.

More Info

15. Elixir Machine Learning


elixirElixir is a dynamic, functional language that is used for the scalable and useable applications. It is also a general-purpose, functional and concurrent programming language that is used for building distributed, fault-tolerant applications. Various big companies like Moz, Bleacher Report, E-MetroTel, Discord, Pinterest, The Outline, Inverse, Divvy, FarmBot, PagerDuty are using the Elixir language of machine learning.

Machine Learning Project With Elixir

  • Deep learning with Elixir.
  • Neural network project
  • Matrix product calculation with Elixir
Important Features
  • Elixir runs on lightweight threads that exchange information through massage.
  • Elixir provides supervisors with fault tolerance.
  • It is an A growing ecosystem that offers to create projects, manage tasks and, run tests.

More Info

16. Swift for Machine Learning


SwiftThe swift machine programming language is a compiled programming language of Apple Inc for the iOS, iPadOS, macOS, watchOS, tvOS and, Linux users. It is the fast, fantastic and interactive programming language to combine all modern language. It is a new programming language for the coders that runs on most of the hardware.

Machine Learning Project With Swift

  • You can develop many machine learning project for iOS users using Swift. 
  • It supports many other IoT platform so you can develop any project using Swift.
Important Features
  • Swift supports closure support.
  • This Protocol-oriented programming can be used for any purpose.
  • Swift uses the same runtime as C.
  • It can compare with many other programming languages.

More Info

17. Perl Deep Learning


Perl Deep LearningThe Practical Extraction and Reporting Language or Perl is a general-purpose, high level, interpreted and dynamic language. Larry Wall developed this Unix scripting language in the year 1987. It borrows features from another programming language like shell script (sh), AWK, sed and even C. After 30 years of development, it offers 25,000 extensions on CPAN.

Machine Learning Project With Perl

  • There are many big projects like DuckDuckGo, Slashdot, Craigslist, IMDb, LiveJournal, and Ticketmaster are using Perl.
  • If you want to make any portable project on Windows and Unix you can use Perl.
  • You can implement the project with other projects written by C programming language.
Important Features
  • Perl supports many Source Code Editor for developing projects.
  • It is an activity developed software so you can contribute if you want. 
  • There are over 300 groups of the forum for Perl support.
  • You can use its 25000 extension module.

More Info

18. Rust Programming Languages


RustRust is a machine learning programming language that empowers the coders to built efficient and reliable software. It is a fast and memory-efficient that collect garbage without a runtime error. It enables you to eliminate many classes of bugs at compile-time with its rich Compiler and Editors. Over a hundred companies use machine learning languages like Firefox, Dropbox, Cloudflare, etc.

Machine Learning Project With Rust

  • Redox Operating System
  • Mozilla Firefox
  • Firecracker
  • Cranelift
  • Wasmer
  • Wasmtime
Important Features
  • Rust is a memory-efficient and fast programming language.
  • This robust ecosystem is suitable for the command line. 
  • It is good for network service.
  • Rust is a great fit for large corporations on embedded devices to scalable web services.
  • Rust is an object-oriented programming language.

More Info of Rust

19. Haskell Programming Languages


Haskell Programming LanguagesThe last machine learning language of today’s discussion is Haskell (programming language) languages. It is purely functional, statically typed and general-purpose programming language that used for teaching and research. You can use it in a factory for industrial IoT applications. Haskell possesses several programming language features.

Machine Learning Project With Haskell

  • You can do the various project with Haskell like:
  • ShellCheck: a tool for static analysis. It can be used for shell scripts.
  • Darcs: A version control system.
  • Corrode: C to Rust translator
  • PostgREST: A fully RESTful API from any existing PostgreSQL database.
Important Features
  • Haskell is a non-strict, purely-functional programming language.
  • It is considered as medium level or low-level language.
  • Strong data typing is combined with polymorphism.
  • It takes a shorter development time with high reliability.

More Info

20. TypeScript Programming Languages


TypeScript Programming LanguagesTypeScript is a programming language of Microsoft. It is open-source software designed to make a large project. TypeScript is a subset of JavaScript to compile plain JavaScript. This FOSS ( free and open-source) software any best browser, operating systems, and platform. It is mainly developed to implement JavaScript applications. It has developed to fix the limitation of JavaScript for Microsoft and external users. You can use it also as a Machine learning language.

Machine Learning Project With TypeScript

  • You can develop many projects of JavaScript using TypeScript language
  • Microsoft uses this for various ML project.
Important Feature of TypeScript
  • TypeScript compiler can be used on the JavaScript host.
  • You can install a command-line TypeScript compiler as a Node.js package.
  • It has interaction also with Visual Studio 2017, Eclipse, Vim, WebStorm and Emacs.

More Info

Final Thought


All the machine learning language is not required for developing ML project. The endeavor was to make you introduced to several ML languages. Instead of developing any project of taking any decision you can consult with machine learning service providers to get the idea of the details. At my subsequent writing, I will try to cover all ML ideas to start a new project. Until then keep in touch.

Hawlader
I am also a freelance blogger and real worm of Apps. I love to experiments various apps and games on my android and iOS platform. So here I want to share my cumulative experience and findings regarding various types of apps and games. I am optimistic that this apps review will help the online reader to find the best apps and games for the particular OS.

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