Machine learning has been attracting a lot of attention in recent times as one of the most revolutionary aspects of Artificial Intelligence (AI). There have also been leaping advancements in the technology and software being used in the industry. In harmony with the rapid pace of developments in machine learning, there is an equally wide variety of programming languages currently being used in the field.
Both novices and seasoned pros will agree that language selection is a crucial part of any machine learning project. Admittedly, with so many languages to choose from and conflicting information on which one is best, some significant challenges arise. To shed some much-needed light on the topic, this article explores the best languages for machine learning in 2019.
We will consider six popular machine learning languages, assessing the strengths and weaknesses of each one in relation to practical application within the field. Furthermore, we will examine how they compare to other languages. At the end of it, you will be better placed to make the right choice and make the most out of the language you will choose.
Specifically designed for high-performance computational and numerical analysis functions, Julia is one of the fastest growing languages for machine learning. Everything in its fundamental design matches with the requirements for creating effective machine learning systems. Some of its top features include:
Mathematical Syntax – Julia has its roots in mathematics, a feature which makes it extremely useful for data analysis. Implementing mathematical formulae in code is relatively easy and there is no loss in translation, reducing risk and enhancing efficiency.
High Speed – The loops and functions of this language are so fast that there is a considerable performance difference against most other languages. This makes it most suitable for projects in which latency could hinder effectiveness.
Other benefits include the fact that it is open source and that it has familiar syntax as other languages (Python, R and Matlab), making it easy to use. However, being a relatively new language, it is yet to gain mainstream adoption in developer circles.
Nevertheless, big companies like IBM, Amazon, Google and Microsoft among others are already using it. For instance, IBM and Julia in 2016 developed deep learning systems for eye diagnosis in India based on eye fundus images.
Haskell is rarely considered as one of the default languages for data science. Being a functional language, however, it has noteworthy features that make it ideal for machine learning. Functional programming uses logical functions, dealing with expressions rather than statements. Developers agree on the fact that a programming model that focuses on mathematical functions and logic is well suited to machine learning. Here are some of the reasons for this assertion:
More Consistency, Less Code – Functional languages like Haskell uses less code but still maintain clarity which results in higher efficiency and easier maintenance, an important feature since machine learning has lots of matrix multiplications.
Great at Abstraction and Highly Expressive – Haskell is perfect for abstract mathematics and creates highly expressive algorithms while maintaining great performance.
Considering its key features, Haskell is good for projects that involve the creation of extensive neural networks. In spite of its obvious benefits though, it does not have the same level of support as other programming languages, making it hard to get support. However, it is notable that Facebook uses a Haskell ML model to fight spamming.
One of the top advantages of Java is its high versatility. In fact, its object-oriented language adheres to the WORA principle – “write once, read everywhere.” It is a powerful, scalable and portable language making it ideal for any industry that experiences rapid growth. What are the other specific features that make it perfectly suited to machine learning.
Ease of Use – Developers like it that they do not have to reinvent the wheel every time they write a Java program. It has high portability, allowing a developer to create one app version that will run on multiple platforms.
Robust Set of Tools – Java operates on a vast, open-source ecosystem which includes APIs, frameworks, libraries and the Java Virtual Machine (JVM) among other things. This collection of tools simplifies the coding process and ensures developers have access to everything they need for coding.
Java is flexible enough to allow both experts and newbies alike to use it effectively, thanks to a big global community and availability of useful tutorials online. It is a great choice for projects involving the creation of search algorithms, fraud detection, network security and neural networks. But it is not the fastest language and requires adjustments to work on older platforms.
In spite of being one of the oldest coding languages, Lisp is also among the best choices for machine learning. Developers tend to favor the language due to the level of liberty it offers. It allows for fast prototyping and experimentation, both of which are extremely important in data science. Here are some of its other benefits:
Symbolic Expression – Lisp offers support for the implementation of programs that use symbols for computing, a handy feature for the field of machine learning.
Macro System – The language has a unique macro system or compiler that supports extensive exploration and implementation of intellectual intelligence at different levels. This system can produce libraries of collection types, dynamic size lists and efficient code.
Lisp would be the best choice for a machine learning platform that involves extensive use of symbolic information. One of the biggest challenges associated with this language though is that only a few developers are well acquainted with it. Additionally, since it is a vintage language, using it with new hardware and software mostly requires reconfiguration.
C++ is a highly popular language choice for machine learning and for good reason. Just like Haskell, it is a high-level, object-oriented language and it enables developers to write portable programs considerably fast. It also has rich library support which means easy access to many functions. Take a look at its key benefits in relation to machine learning:
Speed – C++ is one of the fastest programming languages since it can communicate at the hardware level, which enhances code execution time. For time-sensitive machine learning projects such as neural networks, this is a worthwhile feature.
Code Reuse – Since the language supports code reuse during development, it saves both time and costs that would have gone into writing code from scratch.
C++ also supports the extensive use of algorithms and offers significant efficiency in using statistical techniques. It is also ideal for machine learning models that require high speed and low latency such as gaming platforms and robot locomotion. Unfortunately, it is relatively poor at multi-tasking making it suitable only for the implementation of core systems or algorithms.
The most popular programming language for data scientists at the moment is Python. Data from Houseofbots showed that 57% of machine learning developers and data scientists are using the language. Its high popularity to a great extent revolves around versatility and ease of use.
In the realm of machine learning, it offers the following key benefits:
Vast Libraries – Due to its widespread popularity, Python has lots of prebuilt libraries for almost every need. Access to dedicated libraries such as Pybrain for machine learning saves developers the time they would have used to code from scratch. Since it is consistently winning the hearts of millennials, it also has a strong and active community of supporters who keep extending its host of resources.
Less Code – Compared to most OOP languages, Python uses significantly less code. This comes in handy for machine learning, a field which involves lots of algorithms. It can implement even the most complex logic with limited code. Its interpreted approach also fosters a ‘check as you code’ methodology which reduces the possibility of error.
Python has become the language of choice for coders all around the world. Some of the most popular sites developed using Python include Quora, Reddit and YouTube. In machine learning, it is often the top choice for developers working sentiment analysis and Natural Language Processing (NLP). Notably though, the language is a lot more fragmented than what others used in the field of machine learning which tends to lower productivity.
Choosing the Best Language
The above analysis of popular programming languages in the field of machine learning reveals that each one has its merits as well as drawbacks. Therefore, there can be no direct answer to the question “Which one is the best?”
Rather, the superiority of one language over another will only come to the fore when using it practically. Depending on your level of expertise and the nature of the project you are working on, different languages may offer varying benefits. The most important aspect is to identify the strong points of specific languages that suit your project best so as to optimize performance.
Latest posts by Janica San Juan (see all)
- Machine Learning for Content Filtering – Winning the Battle against Harassment and Trolling - January 30, 2020
- 7 Steps to Correct Data Preparation for Machine Learning - January 22, 2020
- Machine Learning Chatbots: What You Should Know about Neural Conversation Agents - December 18, 2019