Best Languages for Machine Learning in 2020 - Updated List
There are many languages readily available for a engineer to pick and start developing their own work. Checkout the best languages for machine learning in 2020.
Artificial intelligence is an art of creating intelligent systems through science and engineering. These intelligent systems can be in the form of hardware, such as robots or robotic arms, that could help in performing repetitive tasks, as software, like machine languages, algorithms, or a mix of both. For example, autonomous machinery that can take its own decisions based on certain events.
Machine learning is a part of artificial intelligence, whereby computer programs are developed, which can help in analyzing available data to assess trends and experiences. Folio3 provides machine learning solutions, along with the best languages for machine learning. To find out more information on machine learning, and artificial intelligence itself, you can also search many other websites, for example, Expert Systems, that break down and define the terms in an easy language for a better grasp.
Data Mining is also a branch of learning about technologies and applying it scientifically to different situations being presented to us. In its actual terms, it is meant to describe the extraction of important information from a large pool of available data. It works in different patterns in different events and is an extension of the business analytics that is present in the world today. Data mining works alongside machine learning as a service to construct different models to see how the available information can especially help in predicting the future.
The Top Most Popular Languages Trend for Machine learning and Artificial Intelligence in 2020
The interest of a topic is gaged by how popular a certain term has been in web searches. We have calculated the results, through Google trends, for the past 12 months and have colour coded the results for a better understanding of the viewers.
According to calculated trends, over the past 2 years, R peaked the most over the whole year and has proved to be the most popular, by touching the 100 (maximum points) bar quite a few times. This means the people have searched mostly for R when it comes to programming languages.
Following R, the next two most commonly searched for items are Python and Java. This shows that after R, Python, and Java are the most popular programming languages as the trends have presented.
Analyzing the trends have also brought into light that Lisp does have searches on it, though, they are almost next to nothing, making it not too popular of interest among people. And lastly, we can see that Prolog is a deadline, still at zero, and has had no search for it over the past twelve months, making it totally unpopular in these past two years.
This trend analyzes all the areas where a certain search term is the most popular. Some countries search for a certain term more than others, and we can also see which terms are most commonly searched for.
What we have analyzed looking at the areas presented above is:
Python is most commonly searched for in countries such as Israel, Taiwan, Russia, Singapore, Hong Kong, South Korea, Norway, Iran, Ukraine, Switzerland, and many others. As it is also quite visible, Python is extremely popular all over the globe, and most countries rely on searches related to Python for their work.
Another analysis that is presented to us is the key terms or the search keywords that people use across the world to search for Python. Some essential key terms related to Python are: “python for,” “python list,” python string,” “python with” and “python if” that have been searched for the maximum number of times, as these have been given a more than 50 weightage on a scale of 1 – 100.
There is also a list of key terms that are on the rise in the searching area, and for python, these terms include: “python 3.8”, “monty python terry jones,” “new colt python,” “google colab” and “geeksforgeeks python.” These words are being searched in multi-folds and are still on a rising scale, because, as we can see, people know a lot about python already and are not new to it, they just need further information on how and where it is used today.
The countries where R is the most popular search are Thailand, South Africa, Philippines, Canada, Brazil, Indonesia, Italy, United Arab Emirates, Australia, and New Zealand, among a few others. These areas search for R as a term more than even Python. It is found to be the second most searched term in the world.
The certain key terms related to R that people use to search for it are: “type r,” r and r,” and many others. Some rising keywords that are being searched the most and have kept on increasing are: “xhamstervideodownloader apk for mac download r,” “insta360 one r,” among others.
Lisp is one of the very lowly searched items and is not quite popular in any certain country of the world. Yet, some of the countries where it is searched and is known are Canada, South Africa, Bangladesh, India, Philippines, Singapore, Israel, South Korea, Nigeria, and the United States of America, though, in China, it is has a search interest of complete 100. The trends do show that the Asian continent is more popular, with regards to Lisp, than the others.
These countries use these key terms to search for when looking for material related to Lisp: “autocad lisp,” “common lisp,” “lisp meaning,” “lisp programming,” “lisp language” amongst other terms. These keywords actually depict how people want to find out more about lisp, and are relatively new to this term, leading to them trying to search about it. The rising in popularity keywords, however, are: “lisp tính tổng diện tích,” “lisp cắt dim” and others in the same foreign language, which makes it even more evident that it is only popular in some specific regions.
As the interest trends over time showed, Prolog was hardly ever searched, gaging the least amount of audience on the web. People usually do not even search for it as such. Though some countries need to search upon it, for example, Indonesia, Peru, Serbia, Canada, South Africa, Bangladesh, India, Philippines, Singapore, and Israel. Again, we can see a trend that the major technology hubs are not interested in this term; rather, the developing countries and regions that are on the growth stage are searching for it.
Some of the keywords people search for, regarding Prolog, are: “online prolog,” “prolog program,” “prolog programming,” “prolog download,” and “prolog language.” This, again, like Lisp, shows that people are new to this language and need to find out more about this term. The terms that people are more interested in for Prolog are also not that many, and not quite related to the language itself, giving the idea that it is not too popular, and if it wants to succeed, it needs to work in a better way, and give customers what is needed from these machine languages.
Being the 3rd most popular from the group of languages listed above and depicted in the map, Java mostly has its popularity in regions such as Bangladesh, Nigeria, Vietnam, Kenya, Ukraine, Serbia, South Korea, India, Hungary, and Morocco. This shows a good spread of its search over the web, and its popularity is still seen to be on the rise.
Java has a lot of key terms that it has been searched by, for example: “string java,” java download,” “java array,” “list java,” “java program,” and “java minecraft.” Some keywords that are rising in searches since last year, for java, are: “java jazz 2020”, “minecraft java edition,” “tlauncher,” “java minecraft” and java 11 download”, etc. These terms show that java is needed by most people to either play a program in their machines or play some games where java is required as a plug-in, more like in a supporting program way, though it is pretty popular around the world.
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The Top 10 Best Languages for Machine Learning
There are many programming languages readily available for a person to pick and start developing their own work. For machine learning and making, too, these programming languages are highly essential. Whether it be gaming software, artificial intelligence works, or any other computers and gadgets related to technological work, one has to know programming languages. Checkout our best machine learning r packages if you want to learn a programming language, especially R, for web development, with more than 20 options to choose from.
Although, we do understand that some people may not want to learn a language on their own, which is why there is always an option of outsourcing the work. Folio3 predictive analytics solutions are available for machine learning and cater to multiple different aspects of it, such as Product Conceptualization, Predictive Engineering, Maintenance Analysis, Design, Automation, Data Acquisition and Analysis, Product Planning, Utilizing Operational Data and many other services that pertain to this certain field. They also help in finding which best learning software and tools are available for the developer this year.
Machine learning helps technological advancement and development in many different sectors of an economy, for instance, the Banking Sector, Health Sector, Transportation Sector, Trading Sector, Food Sector, and of course, the IT sector itself.
Different programming languages are used for different types of machines that are performing different kinds of tasks. The top 10 best languages for machine learning are as follows:
Python is an open-source platform that provides a tremendous amount of community support, and has an extensive library, dealing with a multitude of different systems. It can scale different complex applications and lets the user test their ideas and build their prototypes in the least amount of time. The only drawback it may have is that it is slower and not preferred when it comes to mobile computing. Otherwise, it is the number one choice for machine language around the globe, and the most preferred to use by millions of developers and big companies.
Python has another higher level of a framework known as “Django.” This is also an open-source and completely free for people who want to start working on it. It is an all-inclusive framework that has everything available for you under one roof, and it encourages rapid development and a platform for pragmatic design. Many big applications nowadays use this framework for their development, such as Mozilla, Pinterest, National Geographic, and many more.
C++ is a machine language that is extremely efficient in resource management and is super easy to use for new developers or even professional ones. It is an extension of the former “C” language, but a step higher this time. It was initially developed for embedding and system programming on a larger scale, with performance, flexibility, and efficiency being its core values, though, over time, it has developed into many other key areas of computing and software.
C++ provides a software infrastructure that is resource-constrained, which is much faster than other languages and gives the developer the opportunity to work on multi-device and multi-platform applications. It runs close to the system hardware and has a support backup in case of function overloading and real-time mathematical simulations. Although, it does not work in run-time checking, strict type checking, garbage collection, and has a smaller library.
Some opinions of the C++ language also indicate that it is a not a great choice for the back-end functioning of a website, and only in a few cases would it work, for example, if an extremely efficient type of resource management is required.
Banks could use this kind of programming for their databases, though, where calculations are the essence of the job. Even Google and Amazon could well use C++ for their backend development, but some areas where it has really proved successful are the video games sector, SQL servers, and desktop applications.
Java is another great name after Python in the development area, used worldwide as one of the best languages for machine learning. It is not only easy but also object-oriented and has a general-purpose outlook to it. A Java compiled code can usually work on any Java application or virtual machine and does not need any specific underlying computer system. Last year, it was termed as one of the most popular languages in client-server applications, with a net of 9 million developers working on it.
Java is not only fast, but it is also reliable and highly secure. Many of the applications around us do not even work unless you have Java installed in your computing facilities. Many large companies have already adopted and are working on Java, as is the Android App Development, which shows how big of a market value Java has, that it is used in one of the most used application systems.
It has an abundance of open-source libraries, a stack, and an automatic memory allocation system, has a security manager that accesses and keeps a check on the classes, supports the concept of multithreading, and is an ideal source for distributed computing. The only negative features of Java are that its memory management is expensive and does not have templates available for data structures, but the JVM feature gives it a high degree platform of independence.
Java is sure to bag you the best and most number of job opportunities, and it provides a solution to every possible problem existing within its ecosystem. As already mentioned, Android is using Java, so is it an important back end for the Oracle platform. Thus, using Java is not only beneficial for the developer but also for the company itself.
C# was developed by Microsoft as a general-purpose programming language and got fame in the 2000s for supporting object-oriented programming. It is one of the widely used languages for backend frameworks as well, and for the .NET framework, it has claimed to be one of the most powerful languages.
It has a great ability to work along shared codebases; it is automatically updateable, has a syntax that is similar to C, ideal for Windows development of all kinds and has a very quick execution and compilation time. Though some negative aspects it does have attached are the unsafe blocks, has memory deallocation and no garbage collection, is less flexible as compared to other languages, takes time to learn and work with, and error resolution takes up a lot of effort and time.
It is well suited for applications on Windows and even its mobile apps, along with working on Android and iOS, but for that, it takes help from the integrated development environment. It can be used to build games using Unity, and many popular websites like Dell, Bing, and Visual Studio currently use the C# programming language at their backend.
The popularity of R has been proved earlier in the article as well, and we would still emphasize on R being one of the most famous and commonly used languages for programming around the world. It is especially used in machine learning and data analysis and is capable of creating powerful and great learning algorithms and frameworks, as it also works on graphics, apart from statistical computations.
It seamlessly works on different operating systems and is an open-source and free language. Being highly extensible, it is also very comprehensive when it comes to statistical analysis and provides a highly powerful ecosystem. Although it does lack security features, with no strict guidelines for programming, its memory management is poor, and some packages offered by it are not up to the level at all.
R is ideal to be used by people who want to join big companies for analytics purposes or by statisticians who want to compute data such as regression analysis, classifying information, and decision trees, with also high demand in the bioengineering and bioinformatics fields. One time projects like reports and researches could make good use of R.
Julia was always designed to be a high performance and fast language. It is highly efficient, works on multiple platforms, and is quite interactive and dynamic in terms of scripting. It is easy to express patterns on it, which are functional and object-oriented too, because it has a multiple dispatch mechanism.
Julia is very easy to use for all levels of developers because it has a high level of syntax, which makes it easily accessible and understandable. It is open-source and free, under the license of MIT, and all information and source codes for it are available on GitHub. Though, debugging on it is a problem of its. Moreover, Julia’s allocation and garbage collection is low, so is the memory management system it possesses.
Go is also widely known as Golang, and is similar to C++, having fantastic speed, and compiles to machine code, making it even faster. It is relatively simpler to other languages, can do great work in the concurrency area, and over the past few years, has gained quite a mindshare.
The advantages Go has is that it is backed by the Google company itself, is easier to learn due to syntax and more secure, and has a smart method of documentation. But it finds it difficult to work on complex programs, has next to zero versatility and library support, and as much as the working environment seems exciting, it is equally hectic and chaotic, too.
There are also, however, not many varieties of packages available, and designing a stable architecture of the server is pretty difficult. It is also not as common as Java and is only mostly seen being used by the startups of Silicon Valley.
Erlang is another programming language used to build soft and real-time systems that are massively scalable. The requirements of it would be on high availability, and it is open to working with other languages.
Erlang has an extensive library and designing principles that help develop systems easily and in lesser time than other languages. It has a distributed database, along with debugging tools and better memory management. Though, it still tends to be slower than other languages in terms of providing solutions and is mostly only useful for bigger projects.
Most of the users of this machine learning language pertain to the telecommunication industry, the banking sector, e-commerce, computerization, and instant messaging. Without a doubt, it is the same programming language that is used for applications that use concurrency, for example, WhatsApp, for whom it works alongside another rlanguage known as Elixir.
Scala is one of the main languages that have the support of the Apache Spark platform. It is a comprehensive region and proves appropriate libraries for the big data processing, along with its functionalities and analysis of machine learning. Using Spark, Scala makes the development, designing, coding, and deployment of machine learning algorithms in the best possible manner.
Like many other programming languages, Scala is also well-versed in computations and arithmetic, with having good control over number generation, linear algebra, and other scientific computing. Though its allocation methods are not efficient, with no great memory management, making the whole system suffer, it uses the virtual machine of Java in run-time, and so is faster than even Python.
For these advantageous reasons, Scala is a popular language and is increasing in its popularity with companies dealing in machine learning and big data. Some data libraries worth mentioning for Scala are the Saddle, which helps it deal with automatic alignment of data, filling in missing values itself and support data manipulation in 2D structures. Aerosol is another one of its famous libraries that is user-friendly and is known for a high speed due to being a GPU and a CPU – accelerated library.
Best Languages for Machine Learning on Reddit
As proved by the above comments gaged from Reddit, Python is the most recurring name, which is seen, time and again, as the first choice of most people. From our earlier trend analysis, as well as from sources that have listed the top 10 best languages for machine learning, and now from these comments as well, we can clearly see that Python is the most famous programming languages of all, though, C++, Julia, and R are not so far behind, as some people have referred to them as their preferred choices too.
Best Backend Languages for Machine learning
The backend of any system is the server-side of the website that cannot be seen, but does it work in the background. The backend is important for organizing the data, storing information, and keeping everything in working condition at the front side for the client. It is constantly working with the front end in sending and receiving information for smooth running. The top 10 backend best languages for machine learning are as follows:
- Ruby on Rails
What functional language is best for machine learning?
Python has been declared as one of the top and easy-to-use programming language across the charts for the best languages for machine learning, having 57% of the world’s data scientists and machine learning professionals using it commonly, as declared by multiple sources. You can find it in desktop apps, in network servers, in web applications, and many different areas, and is said to keep on growing further in the programming language region.
As described above in the article, Python is an easy-to-use language, which gives you a great platform to create your own systems and backend framework, as well as the opportunity to test your idea in a short span of time. So if you are looking for quick solutions to your ideas and want to develop something great and are short on time, Python is your best bet. Though, R, Java, and C++ are also readily available free options.
Is C++ good for machine learning beginners?
C++ is a great language for developers who are steady in web development and already have the experience, but for beginners, it is not. Even though it is flexible and reliable, it is still built for large scale purposes, and beginners should not jump that high at their first go. Also, C++ is prone to bugs and debugging it becomes a problem for beginners, as it consumes a lot of time and effort. In addition, the C++ machine learning language is very fast, but a beginner would not be that fast in his work, making the action slower, which means there is no use of using the fast C++ language in the first place. People prefer Python or R as the best languages for machine learning for beginners.
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