Maybe you want to work on your machine learning skills or make a career transition into a data science job role or a machine learning developer. You are wondering what would be the best language for machine learning and how to begin, with so many programming languages to choose from.
As an IT or analytics professional, the best way to begin your machine learning journey is by taking a Machine Learning course. Even as a developer, you can kick-start your machine learning journey with formal learning. Register for a machine learning certification at an institute of your choice and watch your career grow.
How much programming knowledge is required to learn Machine Learning
If you are new to machine learning you may want to know how much programming knowledge is required. What is the depth of experience you are required to have?
The level of programming knowledge for machine learning depends on its use and your data science job role and developer responsibilities.
Programming is needed for you to implement machine learning models, while mastering the concepts is sufficient for your work in project management. Programming knowledge is not all that necessary. Most languages have dedicated machine learning libraries as well as scripting environments that help you to implement machine learning algorithms. However, a basic understanding is a must. So let us begin by exploring what the best language for machine learning is.
Best programming languages for Machine Learning
There is no singular best programming language for machine learning. Each language has its pros and cons. It also depends upon the individual personal experience or job. Data scientists and developers believe it depends on what one is trying to build, your educational background and experience, and the purpose of involvement in the machine learning project.
To begin with majority of data scientists and machine learning developers prioritize Python language for development. It is the most used language by machine learning developers. It supports multiple frameworks and in-built core libraries dedicated to machine learning tasks, making coding easy. Developers find Python easy to learn and code as it has a simple syntax.
Python is popular because of its flexibility while coding and its platform independence. It is scalable, lightweight, versatile, and a simple, general-purpose, open-source language that can power complex scripting. It supports object-oriented, functional, imperative, and procedural development paradigms. Its most popular machine learning libraries are TensorFlow and Scikit.
C/C++ ranks second, both in usage and preference.
It is an efficient and fast language, the reason it is popular among machine learning developers. You can control single resources starting from memory, CPU, etc. Many of the machine learning frameworks such as TensorFlow, Caffe, wabbit, etc., are implemented in C++. So knowledge of C/C++helps you stand out with recruiters and companies
While C can be used to complement existing machine learning projects, C++ can help you implement algorithms from scratch.
C/C++ is used when speed is important, when there is not any Python library for your use case or when you want to control memory usage because the limits of your systems will be pushed.
Java is used in enterprise development and backend systems. Many of a company’s infrastructure, software, applications, etc. are built with Java. Popular data science frameworks such as Hadoop, Hive, and Spark are also written in Java. Applications built with Java are easy to scale.
Java is also fast, a good thing for machine learning projects.
R is used in many machine learning jobs because it is highly supportive of the machine learning environment. However, it ranks lower in developer preference as a machine learning language.
R is an open-source data visualization language based on statistical computing. As it works on the command line interface and other IDEs, it is popular among professionals who are not exposed to coding, like statisticians or data miners. It has a good collection of libraries and tools that support library management and graphs. R implements machine learning methodologies like classification, regression, decision tree formation, etc.
Scala is even faster than Python and brings the best of object-oriented and functional programming to one high-level language. It was built for the Java Virtual Machine (JVM) and can easily interact with Java Code. Scala is based on enterprise apps, on a large database within a scalable solution, which provides stability.
Scala is a well-known compiled language that makes the executable code work with speed. It has a static type of system, which makes it compatible with Java frameworks and libraries. Its capability of big data-powered applications can carry a huge amount of data. The strong backend language can support a massive flow of data.
Scala offers competitive functionalities through its MLLIB library and enables developers an effective way of developing, designing, and deploying machine learning algorithms by leveraging Spark competencies along with other big data tools and technologies.
The choice of language
The choice of language depends upon the project you are working on — your application area. For instance, machine learning scientists working on sentiment analysis or network security prefer Python. Prototyping, scientific computing, and data science tasks also use Python.
Java is prioritized for applications related to cyber attacks and fraud detection, or less enterprise-driven applications such as NLP and sentiment analysis. Java is used in various data science processes such as data cleaning, data importation and exportation, statistical analysis, deep learning, and data visualization.
Artificial Intelligence in games and robot locomotion are areas where C/C++ is favored because of control, high performance, and efficiency. R is highly prioritized in application areas such as bioengineering, bioinformatics, or sentiment analysis.
The best language will thus depend on various factors. Ultimately you must learn a couple of languages and use the one you are most comfortable with or deem fit for the job at hand. With many languages and frameworks to choose from, you are sure to see your output high with machine learning projects.