Matlab for Machine Learning

We know that, for learning ML, we have various languages and certain programming environments at our disposal. Matlab (Matrix Laboratory) is one of them.

Matlab mainly provides a numerical computing-based environment that supports various languages i.e., it’s multi-paradigm. In addition to it, Matlab also provides a separate programming language that just like the environment is a very versatile programming tool.

In this article, we will look at the importance of Matlab for Machine Learning, and also we will draw a comparison with other languages. Matlab is a programming language in itself. Other than that, we will discuss its similarities and differences with other platforms.

We will have a look at some of the toolkits that Matlab provides for machine learning.

Matlab for Machine Learning

Matlab as a framework is very helpful if you are a beginner. It provides a great platform to learn mathematics like statistics and calculus via programming.

Also, the coding in Matlab is fairly simple for mathematics and matrices involving problems. It is a great platform to work and learn from especially for students or if you work for a company, then. It is the top programming language when it comes to numerical and mathematical computing that is great for students and research scientists.

When it comes to machine learning, Matlab proves to be very helpful. Matlab helps in areas like computer vision, image processing, signal processing, model tuning, bioinformatics, etc. It’s a perfect platform for analysis and data visualization.

If you are interested in practicing and learning machine learning mathematics, this is the platform for you. Although we might use it for just math-based purposes, the framework has packages that help Matlab to provide model-designing environments. One such package is the Simulink.

The environment that Simulink provides is simulation and model-based. It is a graphical programming tool and made for dynamic and embedded systems.

Matlab also allows you to create flow-charts for your models. Simulink allows this. You can design your model and it’s various key steps using the flow-charts. For a better understanding in terms of coding, important features, and certain libraries, Matlab has a large collection of these.

Matlab also uses certain well-known libraries from different languages in its own codes. But before we discuss these, let’s understand where Matlab stands among its other competitors.

Matlab vs Python vs R

Big data analytics is a very big thing, especially it will become the most important thing in the near future. So, the market would demand skills that would include knowledge of versatile languages and frameworks. These three surely hit that bracket. So, having a strong base in all three will definitely come in handy.

Even if you are not all three, try to acquire at least two languages in your arsenal. Here, we have three very popular languages, Python, R, and Matlab. Two of these, python and R stand-out in many aspects and are also very well known in the programming world.

So, we know that all three are very important languages, but we will discuss their features and most importantly, what to learn first.

Let’s talk about python first. If you are a beginner, the suggestions would be to go for python at first.

The reason is that python is a general-purpose language and it can be used anywhere. So, to know about a language that has applications in various fields is always beneficial. It would also help you to strengthen your basic programming concepts and to improve your coding skills.

In machine learning, python helps a lot because of its libraries. Python has an ample amount of libraries not only for machine learning but many other fields.

So, if you want to use a language that can also come in use outside machine learning, then python surely wins over Matlab, as Matlab is a numerical computing language and not general-purpose.

Advantages of Python over Matlab

Python has various advantages over Matlab.

• The code in python is much easier to read and understand than in Matlab, as its python code is quite compact.
• Python being a compact language produces fewer bugs and errors than Matlab and they are fairly easier to solve.
• Python is a much more flexible language than Matlab when it comes to Object-Oriented Programming.
• It is a free and open-source language. Matlab comes under neither, as it’s very expensive.
• Python has a common import statement that allows it to import any library into the code. Matlab does not have that feature.
• Matlab doesn’t show the code of the algorithms it provides in the form of apps. This makes it difficult to debug errors.
• Also, the code is not portable in Matlab.

Advantages of Matlab over Python

Now, let’s go the other way, the advantages of Matlab over python.

• Matlab proves better in providing IDEs and libraries, as you would have all of these at your disposal if you have bought it. In python, you have to install everything separately.
• Python is considerably slower in execution time.
• The Simulink package of Matlab doesn’t have any better alternatives in any other language.
• Matlab provides much more favorable data visualization than any other platform.
• It is a much better language for mathematical computing and the use of math-based algorithms as the coding gets relatively easier for these things and in python, you have to code somewhat more.

Now, let’s talk about R a bit. If you now have a stronghold on python and can perform machine learning using python, then you can start learning R.

Machine Learning and data science, both of these technologies are important to learn if you are into AI or Data Science. Unlike Python, R is mainly a statistical language and is helpful mainly in the analytics and statistics field.

There is a reason why python is recommended to be learned before R is that Python has a smooth learning curve unlike R, which is kind of difficult at the beginning and takes time to get used to.

With Python, students get familiar with some concepts. Also, if you are more into deployment and making algorithms, you should start with Python.

R vs Matlab

Now, let’s do a comparison of R with Matlab. Both of these are clearly math and statistics based languages.
R has complex examples to solve and the language syntax is not that easy to understand if you are a beginner. Matlab has simpler syntax and is much easier to remember than R.

This is the reason R has a steep learning curve.

• R, just like python is an open-source language. Unlike Matlab for which you have to pay.
• Matlab generally proves faster than R in terms of computational speed and also statistical calculations.
• Matlab has applications in various areas like ML, mathematics (Matrix-based calculations), data analysis, etc. Unlike R, that is mainly used in statistical analysis and data analytics.
• For ML, it depends on what you really want to do. Both are equally good in different aspects.
• If you want to do some image classification or any other supervised or unsupervised based task, go for Matlab. For statistical operations in algorithms, choose R.
• Even for data visualization, both prove equally great. Matlab provides Simulink for graphical representation and R provides libraries like Ggplot2.
• Both R and Matlab can use code and libraries written in different languages.

Well, these conclusions really do not mean that one language is completely better than the other. It just shows that it depends on what you want to do and what you prefer.

Matlab vs Similar Platforms

Matlab as we know now that it’s paid and can’t really be afforded by students and by learners who want to learn on their own. The universities or the companies that use it usually buy the Matlab package. So, not really a feasible way to learn.

Just so you know that we have other alternatives that can guide us on a similar track for learning Matlab. These platforms don’t include all the features that Matlab has, but they prove to be good for starters. So let’s have a look at them.

a. GNU Octave

Just like Matlab, it provides a framework and a high-level programming language. People choose octave as a pre-step for switching to Matlab as it’s highly compatible with Matlab. Even the programs written in Matlab mostly prove compatible.

But for vice versa, it’s not true as Matlab does not allow some Octave syntaxes.

Octave shares similarities with Matlab in matrix operations and functions, complex numbers, other math functions, etc. Not all the Matlab functions are made available in octave. Packages like Simulink are only available in Matlab.

Also, if you have studied R and want to do data analytics and visualization in platforms like Matlab, the first step would be to practice on Octave (If you don’t have the means to buy the Matlab package).

Matlab runs better in windows, whereas octave runs usually better in Linux. Also, it doesn’t provide the toolkits available in Matlab, but octave is developing certain packages to replace some of these toolkits.

b. Scilab

Just like octave, Scilab is also a free open-source framework and a numeric computation-based programming language. The difference is, Scilab doesn’t put that much onus on Syntax compatibility with Matlab that much as compared to Octave. It is 98% similar to Matlab, whereas octave is 99% similar.

Also, unlike Octave, Scilab provides a package similar to Simulink known as Xcos.

Although it is a really good choice, people slightly incline towards octave due to its syntactical similarity and compatibility with Matlab.

Both octave and Scilab are great choices.

c. FreeMat

Including the above two, if we do a comparison, this one falls out as the least preferred. The reason is that its development stopped in 2013 itself.

Also, it is only 95% compatible with Matlab as compared to the other two. Same as before, it also provides a framework and is a programming language in itself (numerical computational based).

d. Julia

Julia is a fresh player among these platforms. It only came around 2009 and offers the least amount of Libraries among all platforms and languages.

But, being a newcomer it’s going through various advanced transitions and already has numerous advanced math-based concepts like vectorization of functions, etc.

The primary aim for the platform is to become a combination of all the strong points that all major languages possess.

For now, we can for sure say that it does have a great future unless they decide to stop development. We can also access Julia via the Jupyter page.

e. SageMath

It’s also a high-level programming language with a lot of similarities with python and it’s also free and open source. For larger datasets, Matlab performance wins any day.

SageMath is also undergoing a lot of active development. The documentation has a lot of complaints from the community but the developers are into improvements.

A similar alternative for SageMath would be Mathematica. SageMath uses python that makes it difficult for people who don’t know the language.

Matlab Toolboxes

Matlab in place of libraries provides a long range of pre-programmed toolboxes, which you can test and run in your code. There are a lot of toolboxes, but we will see the ones used in ML.

Classification Learner App

• This app helps in classification techniques. The app helps to import data from tables or matrices and then provides algorithms of supervised learning like SVM, Naïve Bayes, etc.
• You can just select and run the algorithm for the data and visualize it on the go.

Regression Learner App

• This app helps in regression-based techniques.
• It provides algorithms for linear, logistics, etc., regressions.
• The same process is there, we just need to import the data, select the algorithm, and run the algorithm.

Descriptive Statistics and Visualization

• This includes various statistical methods like methods of central tendency, distribution methods, etc.
• You can visualize your data in whichever graph form you prefer.

Probability distribution

• We can evaluate probability functions.
• There are various types like discrete distribution, continuous distribution, etc.
• We can even calculate the mean, median, and work on various probability functions.

Cluster Analysis

• We have this app for unsupervised learning.
• It will also provide algorithms like K-means clustering.

Hypothesis Testing

• We can perform various tests like t-test, chi-square tests on the data to understand which particular group it belongs to.

Dimensionality reduction and feature selection

• This app helps in feature selection, extraction, it provides methods like PCA.
• It provides several scaling techniques and factorization methods for data.

These were some of the commonly provided toolkits in Matlab for machine learning.

Conclusion

In this article, we saw what Matlab is all about for Machine Learning. We understood Matlab both as a framework and as a programming language and its use in machine learning.

Then we drew comparisons between Matlab, Python, and R, which might be one of the highlights of this article, because of the sheer importance to understand the difference.

We also covered similar platforms to Matlab that are more free and open-source. Then, at last, we saw various toolkits that Matlab has to offer for machine learning.