What is MATLAB ?

What is MATLAB ?

12 December 2019

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.Typical uses include

• Data analysis, exploration, and visualization

• Math and computation

• Algorithm development

• Data acquisition

• Modeling, simulation, and prototyping

• Scientific and engineering graphics

• Application development, including graphical user interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran. The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB engines incorporate the LAPACK and BLAS libraries, embedding the state of the art in software for matrix computation. MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis. MATLAB features a family of add-on application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others

The MATLAB System The MATLAB system consists of five main parts:

Development Environment. This is the set of tools and facilities that help you use MATLAB functions and files. Many of these tools are graphical user interfaces. It includes the MATLAB desktop and Command Window, a command history, an editor and debugger, and browsers for viewing help, the workspace, files, and the search path.

The MATLAB Mathematical Function Library. This is a vast collection of computational algorithms ranging from elementary functions, like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast Fourier transforms.

The MATLAB Language. This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both “programming in the small” to rapidly create quick and dirty throw-away programs, and “programming in the large” to create large and complex application programs.

Graphics. MATLAB has extensive facilities for displaying vectors and matrices as graphs, as well as annotating and printing these graphs. It includes high-level functions for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. It also includes low-level functions that allow you to fully customize the appearance of graphics as well as to build complete graphical user interfaces on your MATLAB applications.

The MATLAB External Interfaces/API. This is a library that allows you to write C and Fortran programs that interact with MATLAB. It includes facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a computational engine, and for reading and writing MAT-files. 

Explore MATLAB Solutions for:

MATLAB for Data Science

MATLAB makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.

  • Access data stored in flat files, databases, data historians, and cloud storage, or connect to live sources such as data acquisition hardware and financial data feeds
  • Manage and clean data using datatypes and preprocessing capabilities for programmatic and interactive data preparation, including apps for ground-truth labeling
  • Document data analysis with MATLAB graphics and the Live Editor notebook environment
  • Apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
  • Explore a wide variety of modeling approaches using machine learning and deep learning apps
  • Fine-tune machine learning and deep learning models with automated feature selection, model selection, and hyperparameter tuning algorithms
  • Deploy machine learning models to production IT systems, without recoding into another language
  • Automatically convert machine learning models to standalone C/C++ code

                                                                                 Why Use MATLAB for Data Science

Exploratory Data Analysis:

Spend less time preprocessing data; From time-series sensor data to images to text, MATLAB datatypes significantly reduce the time required to preprocess data. High-level functions make it easy to synchronize disparate time series, replace outliers with interpolated values, filter noisy signals, split raw text into words, and much more. Quickly visualize your data to understand trends and identify data quality issues with plots and the Live Editor.

Applied Machine Learling:

Find the best machine learning models; Whether you’re a beginner looking for some help getting started with machine learning, or an expert looking to quickly assess many different types of models, apps for classification and regression provide quick results. Choose from a wide variety of the most popular classification and regression algorithms, compare models based on standard metrics, and export promising models for further analysis and integration. If writing code is more your style, you can use hyperparameter optimization built into model training functions, so you can quickly find the best parameters to tune your model.

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