Introduction Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the performance or results of a model to check whether a model is performing as per our expectations or not. There are multiple model validation techniques that are used to evaluate and validate the model according to the different types of model and their behaviors. In this article, we will discuss ... Read More

Introduction Maximum likelihood is an approach commonly used for such density estimation problems, in which a likelihood function is defined to get the probabilities of the distributed data. It is imperative to study and understand the concept of maximum likelihood as it is one of the primary and core concepts essential for learning other advanced machine learning and deep learning techniques and algorithms. In this article, we will discuss the likelihood function, the core idea behind that, and how it works with code examples. This will help one to understand the concept better and apply the same when needed. Let ... Read More

Introduction Hyperparameter tuning in machine learning is a technique where we tune or change the default parameters of the existing model or algorithm to achieve higher accuracies and better performance. Sometimes when we use the default parameters of the algorithms, it does not suit the existing data as the data can vary according to the problem statement. In that case, the hyperparameter tuning becomes an essential part of the model building to enhance the model's performance. This article will discuss the algorithm's hyperparameter tuning, advantages, and other related things. This will help one understand the concept of hyperparameter tuning and ... Read More

Introduction Linear regression is one of the most used and simplest algorithms in machine learning, which helps predict linear data in almost all kinds of problem statements. Although linear regression is a parametric machine learning algorithm, the algorithm assumes certain assumptions for the data to make predictions faster and easier. Homoscadastocoty is also one of the core assumptions of linear regression, which is assumed to be satisfied while applying linear regression on the respected dataset. In this article, we will discuss the homoscedasticity assumption of linear regression, its core idea, its importance, and some other important stuff related to the ... Read More

GV Mobile+ and GV Connect are using the Google Voice for the business and personal phone calls. Both contains the set of features which helps you to communicate with the digital age. GV Mobile+ and GV are the apps which helps the users to make and receive phone call using the Google Voice number. You can send and receive the text messages and can place call also through the regular phone network. GV Mobile+ requires you to have a data connection (WiFi or 3G/4G) in order to place or receive calls. GV Connect does not require a data connection, as ... Read More

Artificial intelligence or Machine Learning has been invented to reduce human workload. As the usage of AI has increased for the past decades, the day is not so far that AI rule over human intelligence. If you're an avid user of AI, stay with us and read the post, as the article will explore many important aspects of using artificial intelligence. Artificial intelligence or Machine learning is a vast subject. But to understand what is inside the topic, you can be something other than an engineer or science student. Therefore, before jumping into the deep discussion about how it ... Read More

Artificial intelligence and nanotechnology work well together to develop real-world applications. Both technologies are new today; many people see AI and nanotechnology as science fiction. We can produce more powerful AI if the two technologies are closely interconnected. People cannot spend a single day with outusing smartphones or smart watches. The personal virtual assistant on your smartphone is one of your smart device's most significant technical advances. The virtual personal assistant works by using artificial intelligence or AI technology. AI is a revolutionary technology that makes devices smart, intelligent and responsive. Another key driving factor beyond the present technological revolution ... Read More

IntroductionIn Machine Learning and Data Science Exploratory Data Analysis is the process of examining a data set and summarizing its main characteristics about it. It may include visual methods to better represent those characteristics or have a general understanding of the dataset. It is a very essential step in a Data Science lifecycle, often consuming a certain time.In this article, we are going to see some of the characteristics of the Iris dataset through Exploratory Data Analysis. The Iris Dataset The Iris Dataset is very simple often referred to as the Hello World. The dataset has 4 features of three ... Read More

Introduction Simultaneous Localization and Mapping or SLAM is a method that let us build a map and locate our vehicles on that map at the same time. SLAM algorithms are used for unknown environment mapping and simultaneous localization. How is SLAM useful? Engineers can use SLAM for avoiding obstacles and also use them for path planning. SLAM software allows robot systems, drones, or autonomous vehicles to find paths in unknown environments and difficult terrains. This process involves a high amount of computing and processing power. SLAM can be useful for mapping areas that are too small or dangerous for ... Read More

Introduction Logistic Regression is a statistical method to predict a dependent data variable based on the relationship between one or more independent variables. It makes use of log odds and with the help of a logistic function, it predicts the probability of an event occurring. It is a classification method. What are Log Odds and Why are they Useful for Logistic Regression? Logistic regression is used to predict binary outcomes. For example, in an election, whether a candidate will win or not, whether SMS is spam or ham, etc. Odds are the ratio of the probability of success to failure. ... Read More