Matlab for Machine Learning

So far, we have seen that, for learning as well as practicing ML, we have various languages and certain programming environments at our disposal for easy and well-documented codes. Matlab, known by its full name Matrix Laboratory, is one such tool for machine learning

. Matlab mainly aims at providing a numerical computing-based environment that supports various languages indicating that it is multi-paradigm. Furthermore, Matlab even facilitates a separate programming language that is just like the environment which makes it a very versatile programming tool.

PythonGeeks brings to you, an article, that will emphasize the importance of Matlab for Machine Learning while drawing a comparison with other languages. As many might not know, Matlab is a programming language in itself. In addition to that, we will discuss its similarities and differences with other platforms and look at some of its toolboxes and their uses. So, without further delay, let us dive straight into the article and get to know more about Matlab.

Introduction to Matlab for Machine Learning

At its core, Matlab is a framework that is very helpful if you are a beginner at Machine Learning or perhaps using Matlab in the first place. It facilitates us with a great platform to learn mathematics like statistics and calculus along the path of learning programming.

Apart from all this, the coding in Matlab is fairly straightforward for mathematics and matrices involving problems which makes it a handy tool. It is an eminent platform to work and learn from especially for students or if you work for a company, as a beginner. It is amongst the top programming languages when it comes to numerical and mathematical computing. All this makes it a great tool for students and research scientists.

When it comes to the domain of machine learning, Matlab proves to be very beneficial. Matlab assists us in domains like computer vision, image processing, signal processing, model tuning, bioinformatics, while the list goes on. It proves to be a perfect platform for analysis and data visualization as well.

In case the practicing and learning of machine learning mathematics interests you, then this is the platform for you. Although we may tend to use it for just math-based purposes, the framework even has packages that help Matlab to facilitate model-designing environments. One such package for instance is the Simulink.

The environment that Simulink provides is simulation and model-based making it quite handy and effective. It is a graphical programming tool and it mainly focuses on dynamic and embedded systems.

Selecting the Right Algorithm

Choosing the perfect algorithm may seem overwhelming—there exist dozens of supervised and unsupervised machine learning algorithms, and each of them takes a different approach to learning. There does not exist the best method or one size fits. Looking for the right algorithm is partly based on trial and error—even highly expertized data scientists are not able to tell whether an algorithm will work without trying it out.

Highly flexible models may overfit data by modeling minor variations that could be noise. Simple models prove to be quite handy to interpret but might have poor accuracy. As a consequence of this, looking for the right algorithm demands trading off one benefit against another, including model speed, accuracy, and complexity. Trial and error collectively account for the core of machine learning

Comparison between Matlab vs Python, and Matlab vs R

Big data analytics is a very vast domain, especially it will certainly become the most important thing in the near future. As a result of which, the market would demand skills that would include knowledge of versatile languages and frameworks mainly emphasizing the leading programming languages. These three surely hit that bracket of the most used and efficient languages. Therefore, we need to have a strong base in all three which will definitely come in handy for us.

Even if you do not master all three, your main focus should be to try to acquire at least two languages in your arsenal. In this domain, we have three well-accepted languages, Python, R, and Matlab. Two of these programming languages, Python and R, tend to stand out in many aspects and are also very well known in the programming arena.

Therefore, we know that all three of these languages are very crucial, however, we will discuss their features and primarily, what to learn as a beginning step.

Let us discuss python first. If you are a beginner, then many experts suggest that you should go for python at first.

The reason for this choice is that Python is a general-purpose language and we can make use of it anywhere. As a result, knowledge, and practice about a language that has applications in various fields, always proves to be beneficial. It would also assist you to strengthen your basic programming concepts and improving your programming skills.

In the arena of machine learning, Python assists a lot because of its extensive range of libraries. Python has an ample number of libraries that are not only for machine learning but many other domains and applications as well.

Advantages of Python over Matlab

1. The coding practices in python are much easier to read and understand as compared to that in Matlab, as its equivalent Python code is quite compact and less tiring.

2. Python, being a compact programming language, paves way for only fewer bugs and errors than Matlab and they are fairly straightforward to solve.

3. Python is a much more flexible language as compared to Matlab when it comes to the Object-Oriented Programming paradigm.

4. It is a free and open-source programming language. Matlab falls under neither of these categories, as it is very expensive to work with.

5. Python has an ordinal import statement that permits it to import any library into the code. Matlab does not have that feature of commonality in the import statement.

Advantages of Matlab over Python

1. Matlab proves to be better at providing IDEs and libraries since you would have all of these at your disposal if you have bought the subscription. In Python, however, you have to install everything separately as not all of the libraries come pre-installed with it.

2. Python is comparatively slow in execution time as it is interpreted.

3. We do not have a better alternative for the Simulink package of Matlab in any other language to date.

4. Matlab facilitates us with much more favorable data visualization than any other competing platform.

5. It is a much-enhanced language for mathematical computing. Apart from this, it allows the use of math-based algorithms as the coding gets relatively simpler for such things, and in Python, we need to code somewhat of a lengthy code.

Comparison of R vs Matlab

1. R, just as we have seen in Python, is an open-source programming language. Unlike Matlab, you do not have to pay for it.

2. As we compare, we may observe that Matlab generally proves to be faster than R in terms of computational speed as well as statistical calculations.

3. Matlab has a wide range of applications in various areas like Machine Learning, Mathematics (Matrices-based calculations), data analysis, and so on. Unlike R, Matlab is not mainly used in statistical analysis and data analytics.

4. For Machine Learning, it depends on what you really want to tackle and solve. Both are equally good in different aspects of this domain.

5. If we want to do tasks like image classification or any other supervised or unsupervised-based task, we should prefer Matlab for such tasks. However, for statistical operations in algorithms, we should opt for R.

Toolboxes of Matlab

Matlab, in the case of libraries, provides a vast range of pre-programmed toolboxes, which can help us to test and run our code. There are a variety of toolboxes, however, we will discuss the ones that we widely use in ML.

1. Classification Learner App

This app mainly focuses on helping us with classification techniques. The app facilitates us to import data from tables or matrices and then stipulates algorithms of supervised learning like SVM, Naïve Bayes, and many more. You can simply select and run the algorithm for the data and visualize it on the go seamlessly.

Training Classification Model in Classification Learner App

a. Involuntarily trains a selection of models to assist you to choose the best model. Model types comprise decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, naive Bayes, kernel approximation, ensemble, and neural network classifiers.

b. Survey your data, specify validation schemes, choose features, and visualize results. By default, the app provides security against overfitting by applying cross-validation. As an alternative, you can select holdout validation. Validation may assist you to choose the best model for your data. Plots and performance measures tend to reflect the validated model results.

c. Export the models to the workspace in order to make predictions with new data. The app always tends to train a model on full data apart from a model with the specified validation scheme, and the full model is the model you export.

d. Generate MATLAB code from the app to fabricate scripts, train with new data, work with the help of huge data sets, or modify the code for further analysis.

2. Regression Learner App

This app assists us in regression-based techniques. It stipulates algorithms for linear, logistics, regressions, and many more such algorithms. The same process exists here as well, we simply need to import the data, choose the appropriate algorithm, and run the algorithm effectively.

3. Descriptive Statistics and Visualization

This toolbox includes a variety of statistical methods like methods of central tendency, distribution methods, and many similar methods. This toolbox helps you to visualize your data in whichever graph form you prefer.

4. Probability distribution

With the help of this toolbox, we can evaluate probability functions very conveniently. There exist various types like discrete distribution, continuous distribution, and many more. We can even calculate the mean, median, and work on various probability functions present in our dataset.

5. Cluster Analysis

We can take maximum benefit from this app for unsupervised learning. It will even provide algorithms like K-means clustering and other unsupervised learning techniques.

6. Hypothesis Testing

We are able to perform various tests like t-test, chi-square tests on the data to understand which particular group it belongs to with the help of this Matlab toolbox.

7. Dimensionality reduction and feature selection

This app assists us in feature selection, extraction while providing methods like PCA. It even provides several scaling techniques and factorization methods for data for effective data analysis.

Let us now look at some of the uses of these Matlab Tools

Task	MATLAB Apps and Functions	Product
Classification to predict categorical responses	Make use of the Classification Learner app to automatically train a selection of models and assist you to choose the best. You can even generate MATLAB code to work along with scripts.	Statistics and Machine Learning Toolbox™
	In order to have more options, you can even use the command-line interface.
Regression to predict continuous responses	Make use of the Regression Learner app to automatically train a selection of models and assist you to choose the best. You can even generate MATLAB code to work along with scripts and other function options as well.	Statistics and Machine Learning Toolbox
	In order to have more options, you can even use the command-line interface.
Clustering	Use cluster analysis functions.	Statistics and Machine Learning Toolbox
Computational finance tasks such as credit scoring	Use tools for modeling credit risk analysis.	Financial Toolbox™ and Risk Management Toolbox™
Deep learning with neural networks to tackle classification and regression problems	Use pre-trained networks and functions to train convolutional neural networks.	Deep Learning Toolbox™
Facial recognition, motion detection, and object detection	Make use of deep learning tools for the purpose of image processing and computer vision.	Deep Learning Toolbox and Computer Vision Toolbox™

Conclusion

With this, we have reached the conclusion of this article that talked about the widely used framework for Machine Learning- Matlab. In this article, we tried to introduce Matlab, both in the light of a programming language as well as a framework. We even discussed the comparisons between Matlab and its competitor languages- R and Python. Towards the end, we came across the various toolboxes and their uses in Matlab. Hope that this article from PythonGeeks was able to clear all your doubts regarding Matlab.