5 Best languages for machine learning?


If you are new to machine learning, the hardest part of learning machine learning is deciding where to start. Whether you are trying to refresh your machine learning skills or are looking to pursue a career solely in machine learning, it is natural to wonder which is the best language for machine learning. With over 700 different programming languages ​​in widespread use, and each has its pros and cons, finding out which is the best language for machine learning is certainly a daunting task. However, the good news is that as you begin your journey as a machine learning engineer, you will begin to find out which programming language is best for the business problem you are trying to solve. would be most appropriate.

How is it important to learn a programming language for Machine Learning? 

A programming background is required if one wants to apply machine learning models to tackle real-world business problems, whereas if one wants to learn the concepts of machine learning, then maths and statistics knowledge is sufficient. It depends on how you want to unleash the power of machine learning. To be precise, it is necessary to understand the fundamentals of programming, algorithms, data structures, memory management and logic to implement ML models. With many in-built machine learning libraries offered by various programming languages ​​for machine learning, it is very easy for anyone having basic programming knowledge to start a career in machine learning. Even if you are not a pro in programming, there are many graphical and scripting machine learning environments like Weka, Orange, BigML and others that let you implement ML algorithms without the need for hardcore coding but Basic programming is a must.

1. Python Programming Language

Best Language for Machine Learning - Python- With over 8.2 million developers worldwide using Python for coding, Python ranks first in the latest annual ranking of popular programming languages ​​by IEEE Spectrum with a score of 100. Stackoverflow programming language trends clearly show that it is the only language that thrives for coding. last five years.

2. Java and JavaScript

Although Python remain the favourites of machine learning enthusiasts, Java is gaining popularity among machine learning engineers who are from Java development backgrounds as they need to apply machine learning to a new programming language like Python or R. Learning is not required. Many organizations already have huge Java codebases, and most of the open-source tools for big data processing like Hadoop, and Spark are written in Java. Using Java for machine learning projects makes it easier for machine learning engineers to integrate with existing code repositories.

 

3. Julia

Julia is a high-performance, general-purpose dynamic programming language that is emerging as a potential competitor to Python and R with several key features, especially for machine learning. Stating that it is a general-purpose programming language and can be used for developing all kinds of applications, it works best for high-performance numerical analysis and computational science. With support for all types of hardware, including TPUs and GPUs, on every cloud, Julia is powering machine learning applications at large corporations such as Apple, Disney, Oracle, and NASA.

4. Lisp

Founded in 1958 by John McCarthy, LISP (List Processing) is the second oldest programming language still in use and was developed primarily for AI-centric applications. LISP is a dynamically typed programming language that influenced the creation of many machine learning programming languages ​​such as Python, Julia, and Java. LISP works on Read-Eval-Print-Loop (REPL) and can code, compile and run code in 30+ programming languages.

5. R Programming Language

With over 2 million R users, the CRAN open-source repository features 12000 packages, nearly 206 R meetup groups, 4000 R programming questions asked every month, and 40K+ members on LinkedIn's R group - incredible programming for the R machine. Language is teaching written by a statistician for statisticians. The R language can also be used by non-programmers, including data miners, data analysts, and statisticians.

I hope you have learned something about the best languages for machine learning. Remember that things change over time, and there is no one-stop solution for every use case of machine learning. The best language for machine learning depends on the area in which it is being applied.

 

 

 

 

 

 

 


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