Are you looking to get into machine learning? If so, you’ll need to master a programming language specifically for this profession. We know you might find it hard to choose a coding language for ML, as there are plenty of them, and it’s not easy to figure out the differences between the options for a newbie programmer.
What is Machine Learning?
Machine learning (ML) allows computers to learn, understand and perform human tasks without being explicitly programmed. It provides systems with the ability to automatically obtain rich insights from large data sets or high-dimensional sensory inputs. Machine learning algorithms search through data to look for patterns; they can determine which actions should be taken to optimize outcomes once they find them.
What are the best programming languages for ML?
You might have heard about Python and R, the two most popular ML choices. However, many others can be just as successful. This blog post will discuss the five best programming languages for machine learning to help you kickstart your career with the language that suits your skills most!
Python
The first language on our list is Python. Python has become one of the most popular programming languages in recent years, and for a good reason. It is a general-purpose language that is easy to learn yet powerful enough for complex tasks. Additionally, many libraries are available for machine learning, making it ideal for this field.
Below, let’s list some of Python’s libraries that can be most useful for machine learning specialists.
- NumPy: This library provides support for scientific computing and data analysis. It includes a powerful N-dimensional array object that can be used for storing data.
- SciPy: This library provides algorithms for scientific computing, including linear algebra, Fourier transforms, and more.
- Matplotlib: This library allows you to create plots and graphs for data analysis.
Interested in the easiest way to learn Python? Here is our guide with a FREE source for learning Python.
R
R is a programming language and software environment for statistical computing and graphics. It consists of a widely used interpreter with an extensive standard library and several popular front-end programs that interactively or link to form complete applications. The R language is widely used among statisticians and data miners for developing statistical software and performing data analysis. Polls, surveys of data miners, and studies of scholarly publications conclude that R is the most popular statistical analysis software among academic researchers and students.
R is one of the fastest programs for statistical analysis. It includes linear and non-linear modeling, classical statistics, time-series analysis, classification, clustering, etc. R is also helpful for making any graph you can imagine. Another reason for R’s popularity is its flexibility and extensibility using the S language, allowing new packages to be easily written.
Java
Java is a popular programming language used to create computer programs. It has been around since the mid-1990s and is still widely used today. Java can be run on various platforms as it is designed with portability in mind. Every Java program executes within its process and does not need to worry about interfacing with components from other programs running on the same machine.
Besides, Java’s core libraries provide extensive support for machine learning. Firstly, there is an extensive collection of regression techniques, including linear regression and logistic regression, and other more advanced algorithms such as decision trees and support vector machines.
Our guide on Java vs. Python comparison might come in handy for you!
JavaScript
JavaScript is used across all application types, including web apps, desktop, mobile, native apps on IoT devices, back-end services, and beyond. And the demand for expertise continues to grow as JavaScript goes beyond browsers to power single-page applications used for chatbots, augmented reality, and more. JavaScript is also used in machine learning, cryptography, data analysis, book writing, and countless other applications.
JavaScript is Turing complete. Hence, it can be used to implement any algorithm in general (and thus also machine learning algorithms). Also, JavaScript has a rich set of libraries for the most common tasks when working with data or implementing machine learning algorithms. For example, ConvNetJS, which is a neural network library designed for convolutional networks, contains a set of common layers and a visualization tool. Another example is TensorFlow.JS, which provides a wrapper around the popular TensorFlow machine learning framework to make it work in browsers.
Compare Java and JavaScript and decide which one to choose for your ML career.
C++
C++ is a general-purpose programming language with imperative, object-oriented, and generic programming features. It was designed with a bias toward system programming (large program size, low memory usage, execution speed) and is also applicable as an intermediate language for high-level software developers.
C++ is used in machine learning. It can be used to build all kinds of machine learning applications like deep neural networks, decision trees, etc. The language is also dedicated to accelerating performance-sensitive kernels for Machine Learning Applications that run on GPU (Graphic Processing Unit) Hardware Devices.
Wonder what the difference between C and C++ is? Here’s the answer!
Why should you learn machine learning?
Machine learning is the future. It’s already part of our daily life, even if we don’t think about it that way. ML is in the devices we own, software we use, and websites where we spend time. Think of smartphones, Netflix recommendations, Google Translate, and other examples where ML improves human life.
So if you want to pursue a competitive career of the future, which is sure to be in demand along with technological development, machine learning can be your best choice. If you want to go into this profession, choose one of the above coding languages and start practicing right away!
