Introduction In machine learning, the data and the knowledge about its behavior is an essential things that one should have while working with any kind of data. In machine learning, it is impossible to have the same data with the same parameters and behavior, so it is essential to conduct some pre-training stages meaning that it is necessary to have some knowledge of the data before training the model. The correlations are something every data scientist or data analyst wants to know about the data as it reveals essential information about the data, which could help one perform feature engineering ... Read More

Various cloud computing service models, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS), are gaining popularity because of their elasticity, on-demand, and pay-per-use characteristics (SaaS). The proliferation of IoT-enabled devices in our offices, homes, and hospitals means we now produce vast data, and in contrast, these cannot be stored on an IoT device. As a result, they have come to rely on cloud computing and cloud storage for all of their data processing and archiving needs. However, cyberattacks are wreaking havoc on this computing model. Providers of cloud computing services can employ machine learning to monitor for and stop ... 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

Locally Weighted Linear Regression is a non−parametric method/algorithm. In Linear regression, the data should be distributed linearly whereas Locally Weighted Regression is suitable for non−linearly distributed data. Generally, in Locally Weighted Regression, points closer to the query point are given more weightage than points away from it. Parametric and Non-Parametric Models Parametric Parametric models are those which simplify the function to a known form. It has a collection of parameters that summarize the data through these parameters. These parameters are fixed in number, which means that the model already knows about these parameters and they do not depend on the ... Read More

Introduction Whale Optimization Algorithm is a technique for solving optimization problems in Mathematics and Machine Learning. It is based on the behavior of humpback whales which uses operators like prey searching, encircling the prey, and forging bubble net behavior of humpback whales in the ocean. It was given by Mirjalili and Lewis in 2016. In this article, we are going to look into the different phases of the WOA algorithm A History of Humpback Whales Humpback whales are one of the largest mammals on Earth. They have a special type of hunting mechanism known as the bubble−net hunting mechanism. They ... Read More

Introduction The process of identifying an object or feature with an image is known as Image Recognition. Image recognition finds its place in diverse domains be it Medical imaging, automobiles, security, or detecting defects. What is MobileNet and Why is it so Popular? MobileNet is deep learning CNN model developed using depth−wise separable convolutions. This model highly decreases the number of parameters when compared to other models of the same depth. This model is lightweight and is optimized to run on mobile and edge devices. There are three versions of Mobilenet released so far.ie MobileNet v1, v2 and v3. Mobilenet ... Read More

Introduction User experience (UX) is how a person or user interacts with a product, service, or system encompassing everything from ease of usage, and its usefulness to efficiency. Today, Machine Learning can provide an intuitive user experience through modeling, customization, clustering, and segregation. In this article, let's have a look at how Machine Learning is revolutionizing User Experience. Why does User Experience Matters? In the case of a business that needs to attract customers or to make sales via a website or mobile app UX is almost needed. The duration of time the user spends on these platforms, their search ... Read More

Introduction Monitoring online systems, especially which are data intensive is extremely essential for a continuous health check, analyzing and detecting downtimes, and improving performance. The percentile−based method is a very efficient technique to gauge the behavior of such a system. Let's have a look at this method. A General Refresher What are percentiles and why are they useful? In statistics, the value which indicates that below which a certain group of observations falls is called a percentile or centile. For example, for a student, if he/she has scored 90 percentile marks, it means that 90% of the students have scored ... Read More