AI also known as Artificial Intelligence, Machine learning in short written as ML, and deep learning (DL) are a few of the top three fast-emerging, great, and intriguing technological disciplines containing a wide range of implementations i.e. applications like self-driving automobiles and face recognition systems. Because of their complexities, understanding these topics may appear difficult. Yet, success in these domains requires a solid foundation in computer science, mathematics, and statistics. Moreover, familiarity with common libraries and modeling tools is required. This article outlines a learning route for AI, ML, and DL, outlining key ideas, tools, and methodologies. This roadmap ... Read More

Introduction Business fraud is a severe problem that may result in considerable financial loss and reputational harm to an organization. Traditional approaches for detecting fraudulent actions are sometimes time-consuming and manual, rendering them useless in detecting fraudulent activity in real-time. Yet, with increased data availability and developments in machine learning technology, firms now have access to more efficient fraud detection approaches. This article will define corporate fraud detection in machine learning, explain how it works, and discuss the benefits and obstacles of using it. Corporate Frauds Corporate fraud refers to the purposeful and intentional deceit or misrepresentation of financial ... Read More

To improve the learning model of machine learning, there are various concepts given in machine learning. Hyperparameters are one of such important concepts that are used to improve the learning model. They are generally classified as model hyperparameters that are not included while setting or fitting the machine to the training set because they refer to the model selection task. In deep learning and machine learning, hyperparameters are the variables that you need to apply or set before the application of a learning algorithm to a dataset. What are Hyperparameters? Hyperparameters are those parameters that are specifically defined by the ... Read More

Machine Learning is a very fast and efficient growing technology in the current world. In our society, human beings are considered the most intelligent brains among all living beings to perform any task smartly. Machine learning is the subset of AI (Artificial Intelligence), which is used to develop algorithms that can be used in a computer to learn from previous data and history and make some meaningful decisions. The popularity of machine learning is increasing with time because machine learning can perform tasks that are complex for a human being. A few years ago, the training and coding of a ... Read More

In the last five years, data scientists have created data-crunching machines by using cutting-edge methodologies. Various machine learning models have been developed that help resolve challenging situations in the real world. With the growth in technologies, various services related to the public and government sectors are getting over the internet. It makes the process fast and the reach of services increases rapidly among the citizens. Google is really making our life easy in every aspect. Whether it is from booking a taxi to finding a dentist near me, all these tasks can be done using the various services of Google. ... Read More

Machine learning is an exciting field that is rapidly growing and providing promising career opportunities. Landing an internship in machine learning can be a great way to gain practical experience and build your skills. But landing an internship in Machine Learning as an undergraduate student is tough as most of the well-known companies like Google, Microsoft, Facebook, and Netflix are looking for Ph.D. students who have publications in prestigious journals. However, machine learning solutions are becoming increasingly popular in different industries. Most of the modern technologies like self-driving cars, voice assistants, AI chatbots, and recommendation systems are all powered by ... Read More

For Machine Learning and Artificial Intelligence, Python has emerged as a well enough and efficient high-level programming language. Data scientists, academics, and developers in various fields embrace it as their language of choice. What, though, makes Python such a perfect combination for these applications and research areas? We'll analyze Python's significance in the machine learning and AI disciplines in this article. The Top Seven Reasons for Python's Popularity in AI and Machine Learning Ease of Use and Readability Python is an easy-to-read and gaining knowledge of programming language, making it perfect for novices. Its simple syntax and readable, herbal language ... Read More

Introduction The quality and representativeness of the data used to train and test a machine learning model significantly impact its success. The distribution of training and test data is a key factor in determining the quality of the data. The distribution of training data is the probability distribution of the input data used to train a machine learning model. In contrast, the probability distribution of the input data used to assess the model's effectiveness is referred to as the distribution of test data. This article will examine the variations in training and test data distributions and how they may affect ... Read More

Introduction Logistic Regression is a widely used statistical technique applied in various fields to model the relationship between a binary response variable and a set of predictor variables. This technique is an extension of linear Regression, where the dependent variable is transformed to a logit function to ensure that the predictions lie within the range of 0 and 1. In this article, we will discuss the implications of having two highly correlated predictors in a logistic regression model and the steps that can be taken to address this issue. Logistic Regression: Dealing with Highly Correlated Predictors Correlation among predictors in ... Read More

Introduction Linear regression is a widely used statistical method for modelling the relationship between a dependent variable and one or more independent variables. It is based on the linear relationship between the variables and is widely used in various fields, including economics, psychology, and engineering. However, certain assumptions must be met for the results of linear regression analysis to be meaningful and accurate. One of these assumptions is the assumption of multivariate normality. Multivariate normality assumes that the residuals, or the difference between the observed and predicted values, are normally distributed. This assumption is important because it allows for various ... Read More