MATLAB for Machine Learning

We are aware that we have a variety of technologies and coding languages at our discretion for studying Machine Learning. Among them is Matlab (Matrix Laboratory). In this article, we will examine the relevance of Matlab for Machine Learning and compare it to other technologies. Aside from that, we’ll talk about how it compares and contrasts to other systems. We’ll take a glance at a few of the machine learning development tools available in Matlab. Let’s start!!!

What is MATLAB?

Matlab is primarily a numeric programming platform that accommodates several languages, i.e. it is multi-paradigm. It has a distinct computer language, which, like the environment, is an extremely powerful programming instrument.

Matlab is a whole software program in and of itself. If you are a newbie, Matlab as a framework might be quite useful. It gives an excellent environment for learning mathematics such as statistics and calculus through coding.

In addition, the programming in Matlab for arithmetic and matrices addressing issues is quite straightforward. It is an excellent environment for working and learning, particularly for academics or if you manage a firm. It is the best software program for numerical and mathematical computation, making it ideal for students and research scientists.

Matlab is quite helpful when it comes to machine learning. It is used in computer vision applications, image analysis, signal processing, model optimization, genomics, and so on. It’s an excellent platform for data analysis and visualisation.

This is the environment for you if you want to practise and study machine learning concepts. Although we may only use it for maths-related applications, the platform includes modules that assist Matlab in providing model-designing settings. Simulink is one such package.

Simulink offers a simulation and model-based platform. It is a visual programming toolkit intended for use with dynamic and embedded systems.

You may also use Matlab to construct flowcharts for your algorithms. Simulink supports this. Using flowcharts, you may create your model and its numerous important processes. Matlab includes a vast number of these for a better grasp of code, crucial features, and specific packages.

Comparison of MATLAB vs Python

Data science is a huge deal, and it will likely become the most significant asset in the coming years. As a result, the industry would seek capabilities such as understanding of many programming languages. These three languages (Python, R and MATLAB) are unquestionably the most important weapons in a data scientist’s arsenal. Having a solid foundation in all three will be quite beneficial. We will now see how they compare with each other.

Advantages of Python over Matlab

Python is considerably simpler to learn and digest than Matlab language since it is lot more concise.
Python, being a concise language, generates lesser problems and mistakes than Matlab, and they are simpler to fix.
When it comes to Object-Oriented Programming, Python is far more versatile than Matlab.
It is a free and open-source programming technology. Matlab falls under neither category because of its high cost.
Python includes a general import line that enables it to include any package in its code. That functionality is not available in Matlab.
Matlab does not display the code for the methods that it offers in the format of applications. This makes debugging mistakes tough. Furthermore, the program is not convertible in Matlab.

Advantages of Matlab over Python

Let’s have a look at the benefits of Matlab over Python.

Matlab outperforms in terms of IDEs and frameworks, as you will have access to all of these if you purchase it.
Python requires that you load everything individually.
Python has a much lower runtime.
Matlab’s Simulink module has no superior counterparts in any other program.
Matlab offers far superior data visualisation than any other tool.
It is a lot better tool for mathematical computing and the usage of math-based methods since the programming for these items becomes comparatively easy, but in Python, you have to write slightly more.

R vs Matlab

Both are unmistakably maths and statistics-based programs.R includes complicated instances to tackle, and the language grammar is not simple to grasp as a novice. Matlab’s syntax is significantly better and quicker to understand than R’s.
This is why R has such a high learning curve.

R, like Python, is an open-source programming language. Unlike Matlab, which requires a fee.
Matlab outperforms R in terms of processing speed as well as statistical computations.
Matlab has uses in many fields, including machine learning, mathematics (Matrix-based computations), data analysis, and so on. In contrast to R, which is mostly used for statistical data processing and data analytics.
It all relies on what you truly want to achieve with ML. Matlab is the way to go if you want to undertake image classification or any other supervised or unsupervised assignment. Pick R for statistical calculations in algorithms.
Both are equally effective for data visualisation. For visual analysis, Matlab provides Simulink, while R provides packages such as Ggplot2.
R and Matlab may both use software and modules written in many languages.

These findings, however, do not imply that one language is superior to the other. It just demonstrates that it is dependent on what you want to accomplish and what you like.

MATLAB Comparison with other platforms:

Matlab, as we now know, is a paid programme that instructors and students who wants to study on their own cannot actually afford. The Matlab programme is often purchased by colleges or businesses that utilise it. As a result, it is not a practical approach to study.

Just so you understand, we have various options for studying Matlab that can lead us in the same direction. These systems do not have all of the functionality that Matlab has, but they are enough for beginners.

It, too, provides a platform and a high-level computer tool, similar to Matlab. People like octave as a pre-step before migrating to Matlab since it is extremely compatible with it.

a. Octave (GNU)

It, too, offers an environment and a high-level coding language, similar to Matlab. People like octave as a pre-step before migrating to Matlab since it is extremely compatible with it. Even applications created in Matlab are usually compatible.
However, this is not the case because Matlab does not support several Octave syntaxes.

Octave is identical to Matlab in terms of matrix operations and functions, complex numbers, various maths features, and so forth. Not all Matlab functions are accessible in octave mode. Simulink, for example, is only available in Matlab.

b. Scilab

Scilab, like octave, is a completely public platform and a coding language centred on numerical computing. The distinction is that, in comparison to Octave, Scilab does not place as much emphasis on syntax consistency with Matlab. It is 98 percent comparable to Matlab and 99 percent identical to Octave.

In addition, unlike Octave, Scilab has a package called Xcos that is comparable to Simulink.
Although it is an excellent alternative, people prefer octave owing to its syntactical closeness and interoperability with Matlab.Both octave and Scilab are excellent options.

c. FreeMat

When we compare the aforementioned two, this one comes out as the lowest desired. The reason for this is that its progress ceased in 2013.

In addition, when contrasted to the other two, it is only 95% compatible with Matlab. As previously said, it also serves as a platform and is a computer program in and of itself (numerical computational based).

d. Julia

Julia is a newcomer to these sites. It was released in 2009 and has the fewest modules of any system or languages.

However, being a newbie, it is going through multiple advanced transitions and has several sophisticated math-based notions such as function vectorization, etc.

The platform’s principal goal is to become a synthesis of all the strengths that each major language possesses.

For the time being, we can confidently state that it has a bright future, unless they decide to halt growth. We may also reach Julia through the Jupyter website.

e. SageMath

It is likewise a high-level programming language with many parallels to Python, as well as being free and open source. Matlab’s speed is unrivalled for huge datasets.

SageMath is also experiencing a significant amount of active growth. The documentation has received several criticisms from the public, but the programmers are working to enhance it.

Mathematica is a comparable competitor to SageMath. SageMath is written in Python, which makes it tough for those who are unfamiliar with the language.

Toolboxes for MATLAB:

Instead of libraries, Matlab includes a wide choice of pre-programmed toolkits that you may evaluate and execute in your program. There are several toolboxes, but we will focus on those used in machine learning.

1. Classification Learner App:

This software is useful for categorization procedures. The programme assists in the importing of material from spreadsheets or vectors and then gives supervised learning techniques such as SVM, Naive Bayes, and others. Simply choose and execute the method for the data, then view it on the fly.

2. Regression Learner App:

This programme is useful for regression-based approaches. It includes techniques for linear, logistic, and other regressions.
The same procedure applies; we just import the data, pick the method, and execute the algorithm.

3. Descriptive Statistics and Visualisation:

This contains numerous statistical approaches such as central tendency techniques, distribution models, and so on. You may see your data in whatever graph format you like.

4. Distribution of probabilities:

Probability functions can be evaluated.There are other varieties, such as discrete distribution, continuous distribution, and so on. We may also compute the mean, median, and different probability functions.

5. Cluster Analysis:

For unsupervised learning, we get this application.It will also include methods such as K-means clustering.

6. Hypothesis Testing:

We may run numerous tests on the dataset, such as t-tests and chi-square tests, to determine which category it falls in.

7. Dimensionality reduction and feature selection:

This programme aids with feature selection and extraction, and it includes approaches such as PCA. It includes a number of data scaling and factorization algorithms. These were among the most generally available machine learning development tools in Matlab.

Conclusion

In this post, we learned what Matlab is all about when it comes to Machine Learning. We grasped Matlab as a platform as well as a scripting language, as well as its application in machine learning.

Then we draw parallels between Matlab, Python, and R, which may be considered one of the features of this study due to the significance of understanding the differences.

We also discussed alternatives to Matlab which are more free and open-source. Finally, we saw the many machine learning development tools that Matlab has to provide.