In the current fast paced world where innovation in technology is the main focus two fields are mostly considered to have significant impacts. These are: "Data Science and Artificial Intelligence". Obviously, both of them are related to the concept of data, but their primary functions are dissimilar. Data Science deals with finding patterns in data for inference for problem solving whereas Artificial Intelligence makes use of that data for developing smart systems. So let’s define both concepts and then we will see how we can compare "Data Science and Artificial Intelligence" in terms of their essential components to figure out how ... Read More

This is an article in which we will look at the difference between data analytics and data science. Data Science Data science is a field in which structured and unstructured data are used to extract meaningful information with the help of different algorithms and scientific methods. One of its fields is artificial intelligence which has now become very popular. The subjects included in data science are mathematics, statistics, science, programming, and many more. Importance of Data Science There are many important factors related to data science and some of them are listed below − Insights are extracted from the ... Read More

Introduction What is Categorical Model? In machine learning models, categorical variables are essential because of the insights they bring. Categorical variables, however, require numerical inputs and present their own set of problems. Categorical encoding is the method through which categorical variables are converted into a form that can be read and comprehended by machine learning programs. ML's Reliance on Categorical Data Categorical variables such as color, category, and kind are crucial to the success of machine learning models and so necessitate careful management and understanding. Challenges of Categorical Variables in ML Machine learning has trouble with categorical variables because they ... Read More

Introduction Measuring and analyzing eye movement data can teach us a great deal about how individuals focus on and interpret visual input. In this article, we will explore the concepts and applications of eye tracking, as well as how it assists researchers in determining where people's attention is focused when shown visual stimuli or interacting with interfaces. The use of eye tracking data as useful input for training machine learning models is presented in an effort to obtain a greater understanding of human behavior and how humans interact with visual content. The incorporation of eye tracking metrics into machine learning ... Read More

Introduction In machine learning, the Weibull Probability Plot Correlation Coefficient (PPCC) plot is used to examine the data's assumed distribution. It helps evaluate the accuracy of machine learning models and sheds light on whether or not the Weibull distribution is a good fit for representing the data. The Weibull PPCC plot is created by contrasting the data's ordered quantiles with the Weibull distribution's quantiles. Scientists can tell whether or not their data follows the Weibull distribution by looking at the shape of the plot. When building machine learning models, this data is essential for deducing the underlying properties of the ... Read More

Introduction The variance of the error terms in a regression model varies across the levels of the independent variables. This phenomenon is known as heteroscedasticity. It goes against the homoscedasticity or constant variance assumption of traditional linear regression. Coefficient bias, ineffective standard errors, and erroneous findings from hypothesis testing are all possible outcomes of heteroscedasticity. Regression model validity and trustworthiness depend on the detection and correction of heteroscedasticity. Researchers are better able to acquire precise statistical inferences, efficient standard errors, and credible hypothesis testing if they are aware of the presence and nature of heteroscedasticity. Role of Statistical Tests in ... Read More

The remarkable progress of machine learning has revolutionized numerous domains by empowering computers to uncover patterns and make well-judged predictions based on data. When it comes to processing images, one particularly powerful tool that has emerged is Convolutional Neural Networks (CNNs). These networks possess remarkable worthiness to efficiently capture local patterns, making them platonic for image wringer tasks. However, to remoter enhance the capabilities of CNNs, an innovative technique tabbed Continuous Kernel Convolution (CKC) has been introduced. In this article, we will delve into the concept of CKC and its significance within the realm of machine learning. What are Convolutional ... Read More

Emotion detection is a fascinating subject of machine learning that has sparked a lot of sustentation in recent years. Understanding and assessing human emotions from text data offers a wide range of applications, including sentiment wringing in consumer feedback, social media monitoring, and developing virtual teammate abilities. Among the several emotion detection methods available, Bidirectional Long Short-Term Memory (BiLSTM) stands out as a powerful tool capable of swiftly capturing the contextual information needed to unceasingly categorize emotions in text. Let's start by comprehending the relevance of Bidirectional LSTM. Long Short-Term Memory (LSTM) is a sort of recurrent neural network (RNN) ... Read More

Introduction Cross Validation (CV) is a way of training machine learning models in which multiple models are trained on a part of the data and then accessing their performance or testing them on a independent unseen set of data. In the Cross-validation technique, we generally split the original train data into different parts iteratively so that the algorithm trains and validates itself on each portion of the data none of them are left out in the process In this article let us have a deep good understanding of the Cross-Validation technique and its significance in improving Model accuracy. Cross Validation ... Read More

Introduction Normality is defined as the phenomenon of belonging to a normal or Gaussian distribution in statistical terms. The normality of a dataset is the test for a dataset or variable if it follows a normal distribution. Many tests can be performed to check the normality of a dataset among which the most popular ones are the Histogram method, the QQ plot, and the KS Test. Normality testing – Checking for Normality There are both statistical and graphical approaches to determining the normality of a dataset or a feature. Let us look through some of these methods. Graphical Methods Histogram ... Read More