Can MATLAB be used for Machine Learning?

Yes, MATLAB is a very good language and computing environment for machine learning. It is actually one of the most commonly used programming languages for machine learning. MATLAB is used by many engineers and scientists for building machine learning models.

Since its design by mathematician and computer programmer Cleve Moler, MATLAB has grown in popularity and usage to become one of the most widely used programming languages in Data Analysis, Data Visualization, Machine Learning, Signal Processing, Image processing, and Computer Vision.

MATLAB is commonly used for machine learning to train models, tune parameters, and deploy to production or the edge. It is also used in deep learning for Data preparation, design, simulation, and deployment for deep neural networks.

MATLAB is heavily used in building machine learning models because of its flexibility. Another strength of MATLAB is that it can call functions and subroutines written in other programming languages such as C and Fortran to increase performance.

There are many companies that use MATLAB for machine learning, deep learning, automation, and many other tasks. Some of the popular companies include ABB, Mahindra, Broadcom, Aberdeen Assets Management, Intel, Microsoft, Cognizant, Bosch, Airbus, Land Rover, Qualcomm, HSBC, etc.

Although MATLAB is used by many companies, its popularity has been decreasing over the years. According to Stack Overflow surveys, MATLAB moved from the third most dreaded language in 2021 to become the most dreaded programming language in 2022.

MATLAB is loved by only 19.16% of developers versus a staggering 80.84% of developers who dreaded it. Seen that MATLAB’S popularity is slowly decreasing, there are other programming languages that you can use for machine learning.

WHAT ARE ALTERNATIVES TO MATLAB FOR MACHINE LEARNING?

There are many programming languages that you can use as an alternative to MATLAB for machine learning. Here are some of the popular ones.

PYTHON

Python has an easy-to-understand syntax that makes it easy to write code. Getting started with Python for machine learning is much easier than with MATLAB. Python is used a lot by developers for machine learning and artificial intelligence.

According to a 2022 Stack Overflow survey, Python is the third most used programming language by professional developers. It is also one of the most loved programming languages with 67.34% of developers who loved it versus 32.66% of developers who dreaded it.

Python has a lot of resources and libraries that you can use for machine learning and artificial intelligence. Here are some of the popular ones.

TENSORFLOW – An open-source library for developing and training machine learning models. Tensorflow also helps developers to build and deploy machine learning-powered applications. It is used by many companies such as PayPal, Bloomberg, eBay, Dropbox, IBM, Coca-Cola, Google, Airbnb, DeepMind, Uber, Snapchat, Qualcomm, Airbus, Intel, Twitter, and many others.

KERAS – an open-source, high-level, deep learning API developed by Google for implementing artificial neural networks. It can run on Tensorflow, Theano, Microsoft Cognitive Toolkit, and many other platforms.

NUMPY – Numpy is the most popular package for scientific computing with Python. It is used for machine learning, data science, visualization, Array libraries, image processing, signal processing, etc. Numpy also powers many other scientific and machine learning libraries.

Other Python-based machine learning libraries include Scipy, Scikit-Learn, Pytorch, Pandas, Theano, Matplotlib, Nltk

JULIA

Julia is a great language for machine learning, it is a high-level, high-performance dynamic language. Julia is used for Machine Learning, Scientific Computing, Parallel Computing, Data Science, Data Visualization, etc.

Julia has a lot of packages for machine learning, some of the popular ones include MLJ.jl, Flux.jl, Knet.jl, AlphaZero.jl, Turing.jl Metalhead, ObjectDetector, and TextAnalysis.jl. These packages will helpful for Deep Learning, decision trees, clustering, pre-trained models, reinforcement learning algorithms, etc.

For example, the Federal Reserve Bank of New York used Julia to make models of the United States economy (including estimating COVID-19 shocks in 2021), noting that the language made model estimation “about 10 times faster” than its previous MATLAB implementation.

Julia is a very fast and high-performance language compared to many programming languages used in machine learning. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.

Julia is the 5th most loved programming language, it is used by many companies for machine learning, these companies include Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, and many others.

R

R is an open-source software environment for statistical computing and graphics. R is heavily used in building machine learning models because of its flexibility. There are many R packages that you can use for machine learning.

Some of the popular ones include Classification And Regression Training (CARET), Data Explorer, Dplyr, Ggplot2, mlr3, Xgboost, Superml randomForest, e1071, and many others. Companies using R include Amazon, Google, Flipkart, Firefox, LinkedIn, ANZ, Accenture, Infosys, etc.

C++

C++ is another great and popular option for machine learning and artificial intelligence. You can use libraries such as Caffe, Microsoft Cognitive Toolkit, MLPack, Shark, Gesture Recognition Toolkit (GRT), and many others.

These libraries are helpful for deep learning, artificial neural networks, classification, regression, forecasting, linear and non-linear optimization, algorithm development, and more.

JAVA

Java has a lot of libraries for machine learning. Some of the popular libraries include Weka, Apache Mahout, Deeplearning4j, Mallet, Spark MLlib, JSAT, Encog Machine Learning Framework, JavaML, Massive Online Analysis (MOA), and many others.

These libraries are helpful for deep learning, classification, artificial neural networks, regression, forecasting, clustering, association rules, recommendation, and more.

CONCLUSION

It can be seen that MATLAB is popular and widely used for machine learning projects. It has many amazing features that make it a popular choice for machine learning, deep learning, automation, image processing, and computer vision.

But MATLAB’s popularity over the years has been decreasing and other languages such as Julia that have modern features are increasingly becoming a better option and slowly replacing MATLAB in some areas.