Known as a subset of Artificial Intelligence (AI), Machine Learning trains a computer to mimic human behaviour using algorithms and datasets. It equips the computer systems to automatically learn and predict using the fed data.
The amount of programming language knowledge you need to learn machine learning depends on your intended ML application. However, considering the global machine learning market is anticipated to grow at 42.08% CAGR during 2018–2024, this is the right time to equip yourself with more knowledge about Machine Learning and its prerequisites.
To get started, honing in-demand programming languages can help you initiate your journey as an ML professional.Â
Wondering which programming language can help you proceed with your ML career?
Let us take you through the most demanding programming languages used for Machine Learning in 2023!
1) R for Machine learning
This functional programming language is extensively used for data visualisation and analysis. In the realm of machine learning, R is commonly used as a supplementary tool. ML engineers can use R to comprehend statistical data. They can implement the corresponding principles to huge amounts of data concurrently. Consequently, R proves to be useful for training ML algorithms to forecast future events.
The solutions deduced with R programming language help ML engineers streamline data. It comprises various packages that prove to be beneficial to ML engineers. These packages are as follows:
- tidyr
- Dplyr
- Ggplot2
- CARET
- PARTY
- MICE
- Shiny
- rpartb
- randomFOREST
- Rmarkdown
2) Python
One of the prominent reasons justifying Python’s escalating popularity is the growing acceptance of machine learning internationally. Its massive library suggests that it is a viable choice for professionals like machine learning engineers, data analytics, and data scientists. Its library ecosystem allows ML practitioners to access, manage, convert, and process data easily.
Its characteristics, like reduced complexity, improved reliability, simple syntax, and platform independence, make it a suitable choice for ML projects. Although ML algorithms can be complex, Python provides flexible and easy-to-read code. Hence, ML engineers can create the most viable solution for solving real-world problems.
Python supports various libraries and frameworks. So, it benefits ML engineers with flexibility and infinite possibilities while working on their projects. Its libraries extend from artificial intelligence to deep learning.Â
Here’s a list of well-known Python libraries for ML projects:
- OpenCV
- sci-kit image
- sci-kit learn
- PyTorch
- TensorFlow
- NumPy
- Keras
- SciPy
- NLTK
- Matplotlib
- Seaborn
3) C++
C++ is also an extensively used popular programming language well-known for boosting performance and efficiently managing memory. These characteristics make it a suitable programming language for ML projects.
It manipulates ML algorithms and comprehensively works on memory management. Due to its efficiency and speed, it is useful for developing fast and perfectly coded ML algorithms. It contains libraries like LightGBM, Caffe, DyNet, and Turi Create that you can use while working on your ML projects.
4) Java
If you belong to a Java development program, you know the significance and popularity of using Java for ML projects. ML engineers with this background need not learn programming languages like R or Python. Java allows ML engineers to integrate with prevailing code repositories.
It is a preferred choice for ML due to its characteristics like improved user interaction, package services, ease of use, accurate graphical representation of data, and easy debugging.
Java simplifies application scaling for ML engineers. So, it is an excellent choice for developing huge and complicated ML applications.
Java Virtual Machine (JVM) is one of the finest platforms for ML. The reason is ML engineers can write the same code on various platforms. Moreover, JVM helps ML engineers quickly develop custom tools.
5) JavaScript
As of 2023, 98.7% of websites use JavaScript on the client side for developing webpage behaviour. There is a growing number of JavaScript libraries for machine learning.
One of its libraries, i.e., math.js, offers great computing power and mathematical flexibility. Moreover, it supports packages developed for other languages like TensorFlow.js. So, its packages and libraries allow JavaScript developers to employ/retrain existing ML models, as well as create new ones. Two more JavaScript libraries useful for ML applications are Synaptic and OpnCV.js.
JavaScript contains various models, including object detection, image classification, and GPT-3. Hence, it provides versatility for various ML/AI tasks. Moreover, it has easy-to-understand software to streamline the process of developing and training neural networks.
6) Julia
Julia is a general-purpose dynamic language equipped with features that are specially developed for ML models. It can create scalable ML apps. You can use Julia to easily and quickly deploy applications at large clusters.
ML tools like Flux, Tensorflow, and MVBase are using Julia to save time for ML engineers. It is developed for deploying elementary mathematics and scientific queries that underlay most ML algorithms.
It contains various tools to support ML programs like performance evaluation, score-based classification, data manipulation and preprocessing, model tuning, and cross-validation.
7) LISP
LISP is primarily developed for AI and ML-based applications. It is the most flexible and efficient ML programming language for solving real-world problems. The reason is it is adapted to specific solutions of ML programmers.
The following features make LISP perfect for handling ML projects.
- Flexibility
- Rapid prototyping capabilities
- Automatic garbage collection
- Dynamic object creation
- Support for symbolic expressions
Conclusion
The choice of a suitable programming language for machine learning relies on several factors. These include the area in which you want to implement, your machine learning project’s scope, and the programming languages your industry uses. You can optimally determine the most appropriate programming language for machine learning through testing, experimentation, and experience.
If you are interested in building real-world machine-learning solutions, check out the Learn Machine Learning By Building Projects course provided by Eduonix Learning Solutions.Â
This course provides hands-on training on building real-world projects through Machine Learning algorithms. It provides in-depth information on practical ML applications, covering 12 exciting projects through which it imparts the key concepts of ML, algorithms, and ML functions. Upon the course completion, you would be able to build real-world machine learning projects, which you can demonstrate in your resume.
