Introduction to Matlab
In this article, we will see an outline on Matlab Features. MATLAB, which was developed in the late 70s in the Computer Sciences department at the University of New Mexico, finally became a full-blown software by the mid-80s. MATLAB was developed focusing on mathematical simulations and hence found applications in control systems engineering, mechanical systems analysis, image processing, and also in academics for learning linear algebra and running deep mathematical simulations. While MATLAB had been great for working with static numerical data in vectors and matrices, through decades of development, it can now read data from flat files, databases, cloud storage, data acquisition hardware and even live financial data feeds. Its capabilities have grown to allow users to perform data visualization and even run complex machine learning models, even create mobile and desktop apps.
What separates MATLAB from the most popular platforms R & Python is that this is not an open-source platform, limiting its use to the corporation, academic institutes, and research organizations. Let us take a look at the features in MATLAB that make it the platform of choice for professional applications.
Top 12 Features of Matlab
MATLAB is very capable in the field of data science and is currently being widely applied in industries ranging from insurance, finance, energy, medical devices, industrial automation, automotive and aerospace in various purposes for business-critical functions. With the recent iterations of the software platform being even more capable of running complex machine learning algorithms, its relevance in data science is expected to increase as we explore applications of ML & AI in our daily lives.
Researchers, engineers, and scientists that are already using MATLAB find it quite easy to move to deep learning owing to the functionality. And this functionality is what could encourage new learners too.
1. High-Level Language
This is a high-level programming language with data structures, control flow statements, functions, output/input, and object-oriented programming. It permits both, rapidly creating speedy throw-away programs, and creating complete, complex and large application programs.
2. Interactive Environment
MATLAB provides an interactive environment that allows iterative exploration, design, and problem-solving. It is a bunch of tools that a programmer can use. It includes abilities for handling the variables in the workspace & importing/exporting data. It also contains tools for development, handling, debugging, and profiling MATLAB files.
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3. Handling Graphics
It offers built-in graphics useful for data visualizing, and tools for generating custom plots. MATLAB holds high-level instructions specially for creating two and three-dimensional data visualizations, animations, image processing, and graphical presentation. This moreover includes low-level instructions that allow users to fully modify the appearance of graphics on top of to build thorough GUIs (Graphical User Interfaces) on MATLAB.
4. Mathematical Functions Library
It offers a huge library of mathematical functions needed for computing statistics, linear algebra, numerical integration, filtering, Fourier analysis, optimization and solving regular differential equations.
5. Application Program Interface (API)
MATLAB APIs allow users to write C / C++ and Fortran programs that directly interact with MATLAB. These include options for calling programs from MATLAB (dynamic linking), reading and writing MAT-files, and for calling MATLAB as a computational engine. The users can utilize MEX API and Matrix API functions to interact with data within the MATLAB workspace.
6. Toolboxes
A ” Toolbox” is a set of functions designed for a specific purpose and compiled as a package. These Toolboxes include MATLAB code, apps, data, examples and the documentation which helps users to utilize each Toolbox. Users can compile MATLAB files to create toolboxes if they require sharing with others. There are separate Toolboxes available from Mathworks, to be used for specific purposes, for example, text analytics, image processing, signal processing, deep learning, statistic & machine learning, and many more.
7. Accessing Data
MATLAB can natively support the sensor, video, image, telemetry, binary, and various real-time data from JDBC/ODBC databases. Reading data from different databases, CSV, audio, images, and video is super simple from an integrated environment.
8. Interfacing with Other Languages
Libraries written in Perl, Java, ActiveX or .NET can be directly called from MATLAB, and many libraries for XML or SQL support can be employed as wrappers around Java or ActiveX libraries.
9. Data Processing
A vast library of mathematical functions for linear algebra, Fourier analysis, filtering, statistics, optimization, numerical integration and solving ordinary differential equations. MATLAB’s numeric routines scale openly to parallel processing over clusters and clouds. Parallel Computing Toolbox distributes training across multicore CPUs graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs.
10. Machine Learning, Neural Networks, Beyond Statistics
Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. MATLAB provides an ML-rich language library, allowing the script to be very short and equally effective as compared to other languages. Automated feature selection and built-in hyper-parameter tuning make fine-tuning the machine learning and deep learning models easy.
11. Text Analytics
Text Analytics Toolbox offers algorithms for pre-processing, visualizing, analyzing, and modeling textual data. It contains tools for handling raw textual data from sources such as news feeds, equipment logs, surveys, social media, and even operator reports. Models created with this toolbox can be used in applications such as sentiment analysis, and topic modeling. Models created with the Text Analytics Toolbox can also be combined with features from other data sources for building machine learning models that use numeric, textual and other data types.
12. Multi-Platform Deployment
MATLAB allows to ‘export’ machine learning models anywhere including Java, Microsoft .NET, Excel, Python, C/C++, CUDA (parallel computing platform and programming model developed by Nvidia), enterprise IT systems, or the cloud. Or deploy models to MATLAB Production Server for integration with web, desktop, database, and enterprise applications.
Conclusion – Matlab Features
Programming platforms R and Python are dominating the data science domain today, but with the superior collective capability in developing and deploying models, MATLAB is expected to grow in terms of popularity and scope of applications. Being a single powerful and integrated platform that allows users to not only analyze data and develop ML models but also create desktop and mobile apps with custom GUIs only strengthens MATLAB’s position in the data science industry.
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