For example, the columns of a matrix may represent the words in a document and the rows may be the sentences, pages, or documents in our corpus. Think, for example, of the representation of a corpus in natural language processing: We require large matrices to represent documents. In turn, this implies that a lot of the implementation work that machine learning techniques require is made much easier in MATLAB. This means that a wide range of mathematical algorithms that are constructed for vectors or matrices are built into MATLAB from the start: cross and dot products, determinants, inverse matrices, etc. In this way, an integer for example can be considered as a 1x1 matrix. In other words, for MATLAB, the basic object of operation is a matrix element. A programming language whose name is actually an abbreviation of “Matrix Laboratory” instills reassurance that matrices are a natural way to express the required computational operations, in a syntax that is close to the original linear algebra notation. I would argue, however, that in many industries and applications, such as aerospace, military, medical or financial, it is an advantage to have a supported and externally validated set of tools, backed by years of development and commercial success.įrom a technical perspective, data scientists and machine learning practitioners require a language that enables them to manipulate objects that are suitable for vector or matrix operations. Partly this may be due to the attention gathered by languages like Python, R, and Scala it may also be the case that being a proprietary language is sometimes seen as a barrier. In the area of data science and machine learning MATLAB is perhaps not one of the first programming environments that come to mind. Why Should you Choose MATLAB for Data Science? Today, MATLAB is a widely-used programming language that many industries trust and whose users can benefit from when trying to integrate machine learning techniques into their applications. These are some of the reasons I wrote Essential MATLAB and Octave, a book to introduce my own physics, mathematics, and engineering students to solve computational problems with MATLAB.
#Learn matlab code
MATLAB provides useful development tools that improve code maintenance and performance as well as integrating with other programming languages such as Fortran, C/C++. Its interactive environment serves as a playground to develop, design, and consume applications with the advantage of having a wide variety of mathematical functions at your fingertips, such as statistics, linear algebra, Fourier analysis, and optimization algorithms among others. MATLAB is a high-level technical computing environment that integrates computation, visualization, and development in a single place. As a result, it is widely used by scientists and engineers in many fields, providing excellent capabilities for data analysis, visualization, and more. Many engineering and science courses have embraced MATLAB as a teaching tool. MATLAB first appeared commercially in the mid-1980s, and its use of expert toolboxes has been a defining feature of the language and ecosystem. I would like to advocate for one of those tools: MATLAB. In reality, there may not be a single best tool to use, and I have long argued for using a toolbox approach to the data science practice.
I have also been asked about Java and I have provided an answer to that query elsewhere. Typically, the question is centered around some usual suspects such as R and Python. A typical question that new data scientists ask is related to the best programming language to learn, either to get a good understanding of coding or to future-proof their skills. The opportunities to solve problems with the use of data are greater than ever, and as different industries embrace them, the available data has been steadily increasing and the number of tools expanded.
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