There are certain languages considered as the Best Language For Machine Learning, AI and data science mastery, Have you started learning them ? Well, In these uncertain times due to the pandemic all the traditional jobs are at risk, therefore, Careers in emerging fields like Machine Learning (including AI)And Data Science, and many other emerging technologies are touted to be the game-changer in career transformation and perceived as advantageous in the economy of the future as too many jobs will be replaced by Robots or Automation though some jobs may not be threatened at all.
Due to a shortage of qualified and skilled labor within these domains, there is a high imbalance in the Job Market. There are tons of jobs available In Machine learning and Data science but very few people have the necessary skill-set to fill these demanding high-tech jobs.
If you are too worried about your job loss or growth in your current profile of work then this is the best time to learn a new skill that will reflect in your Linkedin profile or on Job boards like Monster or Indeed. On these sites, you can also check out the demand(number of jobs) of your Specialized learning skill like Deep Learning, Machine Learning, and AI.
To start learning with these advanced technologies you need to learn certain languages or master them for further progress in the career path. You may already know some of these languages or planning to master any of these listed here. So, Let’s get started with the language list here:
Python can be easily awarded as a reputed leader as 57% of the data scientists and developers use it globally. Python is famous and widely used due to its wide range of libraries all around. So, its always referred as the Best Language For Machine Learning.
This language is the choice of beginners. The libraries available are Teano, Kera, sci-kit-learn, etc that are useful for machine learning, Artificial intelligence, NLP, Etc. There are also the latest existing libraries like NumPy for computation solution and Pybrain for using machine learning within Python.
With easy applicable syntaxes and simple learning algorithms that can be easily learned and implemented. It provides direct access to its coders for predictive analytics. It helps in developing the systems of machine learning practitioners.
2. R – Language
R is a language for graphics representation. It is also a dynamically typed language mainly used for statistical computing, visualization, and performing analysis in machine learning. It is used by a large number of corporations like Uber, Google for data-modeling, and analysis. For Data risk prediction many banks like Bank Of America USe this language.
There are different types of packages involved like Gmodels, Tm, and RODBC majorly used in the field of Machine learning. These packages help in the implementation of algorithms for machine learning.
It’s very popular among Pro Statisticians and also applicable for machine learning tasks such as regression, classification, and decision tree development.
Java is also the most used language by data scientists and machine learning experts. This language is preferable in tasks involving fraud detection, cyber-attacks where popular languages like Python cannot be used.
There are several benefits of using Java-like simple debugging and simplification for work in greater projects, good user interaction, and representation of data in a graphical manner.
Java is highly popular for decades which has still not faded away. The execution part is considerably improved compared with other languages. But it is still a complex to learn and code practically when compared to other languages.
There are a wide range of apps like games, Softwares, and other large scale applications where Java is mostly used for development. It also has the symbol of SWING and SWT, which are extremely helpful in Graphical and UI look to be seen as Engaging and Attractive.
It called a programming language(functional) that will permit future machine learning developments with high speed and precision.
The newest version of Java Also has some more features for machine learning like New string methods – stripleading and striptailing, etc and New file methods like writestring and readstring, etc and Pattern recognizing methods like asmatchpredicate, etc
Scala is one of the important languages supported by Apache Spark. Its a detailed oriented data platform that gives functionalities for processing of big data and ML Analysis through its MLLIB library.
This is the language that provides programmers to develop, design, and deploy machine learning algorithms by efficient usage of the capabilities given by Spark and other big data technologies. Scala programming language provides many well-developed libraries suitable for linear algebra and scientific computing.
5. C, C++
These two languages have been used around for more than a decade. Machine Learning Engineers Are often asked to be able to have an excellent working knowledge of these languages. A large number of companies and projects that have built their systems of these languages and they may be looking for upgrading to machine learning developments – so they especially look for C++ As the requirement for their job role.
C++ has different libraries like small and other Scalable machine learning languages which are not as vast and reliable for Machine learning as compared to Python.
These languages were planned with a tilt towards framework programming in software with a shortage of resources and within large systems with execution, effectiveness, and flexibility of usages.
New developers into machine learning and AI utilize this language for visualization of results by ease the algorithms of machine learning on a dashboard.
Artificial Intelligence Markup Language is an XML dialect used widely as one of the programming languages for AI and ML.
This language was created by Richard Wallace and Alicebot free software community from 1995 -2000. AIML formed the foundation of what was utilized to create or customize Alicebot – a chat-box application based on A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) made as a free software. Because of ALICE, AIML was released under GNU GPL.
Free AIML sets have been created and made accessible by the user community in some programming languages. or example there are translators for AIML available in Java, Ruby, Python, C#, Pascal, Etc.