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In this article, we will learn how to compare two cell arrays of strings of different sizes in MATLAB. Cell Array of Strings In MATLAB, a cell array of strings is a data structure that can store different types of elements. The element of a cell array are enclosed within curly braces `{}`. Syntax We can use the following syntax to create a cell array of strings, A = {'string1', 'string2', 'string3', …'stringN'}; Here, `A` is a cell array of strings that contains N string elements namely, `string1`, `string2`, …, `stringN`. In MATLAB, the cell arrays of strings are ... Read More
Machine learning uses the mathematical approach of Singular value decomposition to comprehend huge and complicated data sets. In this mathematical approach, a Unique Valued matrix A is factorized into three matrices via decomposition. In terms of the components of A, the Singular value decomposition of matrix A can be written as A=UDVT. In this case, S denotes A's singular values, whereas U and V stand for A's left and right singular vectors, respectively. Mathematical Algorithm Given Matrix A find the Transpose of matrix A that is (AT). Find A*AT Find the Eigen Vector of A*AT ... Read More
In an image, a boundary is an element that separates an object from the background of the image or two different regions within an image. Boundary provides information about the shape and structure of objects present in the image. Boundaries are mainly classified into the following two types: Inner Boundary It is the boundary of an object within an image that separates it from the image background. The inner boundary is basically the outline of the object and provides information about the shape of the object. Therefore, by extracting the inner boundary of an object, we can identify and analyze ... Read More
Support Vector Machine (SVM) is a supervised algorithm used widely in handwriting recognition, sentiment analysis and many more. To separate different classes, SVM calculates the optimal hyperplane that more or less accurately creates a margin between the two classes. Here are a few ways to separate hyperplanes in SVM. Data Processing − SVM requires data that is normalised, scaled and centred since they are sensitive to such features. Choose a Kernel − A kernel function is used to transform the input into a higher dimensional space. Some of them include linear, polynomial and radial base functions. Let ... Read More
An optical or visual phenomenon that creates the perception of distortion, movement or any other type of fascinating visual effect by using back and white patterns is referred to as back and white optical illusion. These back and white patterns utilize the way our eyes and brain process the visual information and create an illusion to mislead our perception. The back and white optical illusions use various black and white patterns, shapes, and lines arranged in a specific style. These arrangements are made such that they can create illusion of movement, distortion, depth, and more. In this article, we will ... Read More
PostgreSQL is a popular open−source relational database management system known for its robustness, scalability, and advanced features. Python provides excellent support for interacting with PostgreSQL databases, allowing developers to seamlessly connect and execute SQL queries. In this article, we will explore the step−by−step process of connecting to a PostgreSQL database from Python, running SQL queries, and retrieving results. We will provide code examples, explanations, and sample outputs to help you master this essential skill. In this article, we will learn how to connect and run SQL queries to a PostgreSQL database from Python. Step 1: Install the psycopg2 module The ... Read More
Any dataset used in Machine Learning algorithms may have a number of dimensions. However, not all of them contribute to giving an efficient output and simply cause the ML Model to perform poorly because of the increased size and complexity. Thus, it becomes important to eliminate such features from the dataset. To do this, we use a dimension reduction algorithm called PCA. PCA or Principal Component Analysis helps in removing those dimensions from the dataset that do not help in optimizing the results; thereby creating a smaller and simpler dataset with most of the original and useful information. PCA is ... Read More
Pandas is a Python library that is used for data analysis and manipulation. It provides a number of functions for cleaning and formatting data. In this article, we will learn how to clean string data in a given Pandas DataFrame. We will cover the following topics: Removing leading and trailing spaces Replacing special characters Converting to lowercase Removing duplicate values Splitting strings into columns Merging columns Validating data Removing Leading and Trailing Spaces The strip() method can be used to remove leading and trailing spaces from a string. For example, the following code will remove the leading ... Read More
Recommendation system is a tool in python that suggests items or content to users based on their preferences and past behaviors. This technology utilizes algorithms to predict users' future preferences, thereby providing them with the most relevant content. The scope of this system is vast, with widespread use in various industries such as e-commerce, streaming services, and social media. Products, movies, music, books, and more can all be recommended through these systems. The provision of personalized recommendations not only helps foster customer engagement and loyalty but can also boost sales. Types of Recommendation Systems Content-based recommendation systems These operate on ... Read More
Clustering is a fundamental unsupervised learning technique that aims to discover patterns or groupings in unlabeled data. It plays a crucial role in various domains such as data mining, pattern recognition, and customer segmentation. However, once clustering algorithms are applied, it becomes essential to evaluate their performance and assess the quality of the resulting clusters. Clustering performance evaluation is a critical step in understanding the effectiveness and reliability of clustering algorithms. It involves quantifying the quality of the obtained clusters and providing insights into their consistency and separability. By evaluating clustering results, practitioners can make informed decisions about algorithm selection, ... Read More