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Articles by Mithilesh Pradhan
Page 2 of 5
Non-Linear SVM in Machine Learning
Introduction Support Vector Machine (SVM) is one of the most popular supervised Machine Learning algorithms for classification as well as regression. The SVM Algorithm strives to find a line of best fit between n−dimensional data to separate them into classes. a new data point can thus be classified into one of these classes. The SVM algorithm creates two hyperplanes while maximizing the margin between them. The points that lie on these hyperplanes are known as Support Vectors and hence the name Support Vector Machine. The below diagram shows the decision boundary and hyperplanes for an SVM that is used to ...
Read MoreEnsemble Classifier | Data Mining
Introduction Ensemble Classifiers are class models that combine the predictive power of several models to generate more powerful models than individual ones. A group of classifiers is learned and the final is selected using the voting mechanism. Data mining is the process of exploring and analyzing large datasets to find and explore important patterns, relationships, and information. The extracted information can then be used to solve business problems, predict trends and generate strategic plans by organizations. Ensemble classifiers are used in data mining to perform such tasks. Why do we need ensemble classifiers? Ensemble models(classifiers) can solve many problems and ...
Read MoreCombining IoT and Machine Learning makes our future smarter
Introduction The Internet of Things (IoT) is the network of embedded devices, smart devices, and computers infused with sensors that can communicate with each other as well as send and receive packets of data through the network. These devices can communicate with the real world through sensors and can control or move a system using actuators that are the heart of an IoT system. Machine learning and IoT have a very association in the sense that many organizations using machine learning and Ai based applications rely on terabytes of data captured through IoT and embedded devices. This da can be ...
Read MoreUnderstanding Types of Mean
Introduction The average value of a set of data points, observations, or values is known as the Mean of the data. It is the measure of the central tendency. Mathematically, the mean is obtained by dividing the sum of values by the number of values or observations. It is also called the expected value. The mean itself is not restricted to this simple form but has different types such as the arithmetic mean, geometric mean, harmonic mean, and weighted average. It is given mathematically as, $$\mathrm{Mean=\frac{\sum x}{N}}$$ where, x = set of observations N = number of observations Different ...
Read MoreTypes of Regression Techniques in Machine Learning
Introduction Regression is the technique of predictive modeling to analyze the relationship between the independent variable and the dependent variable. The relation between the target(dependent variable) and the independent variable may be either linear or non-linear. The target is always continuous value and regression is widely used in forecasting, understanding cause and effect as well as in predictive analysis. In this article let us explore various regression techniques available. Regression Techniques Linear Regression − It is the simplest of all regression techniques. In Linear Regression the independent and the target variable are linearly related or dependent on each ...
Read MoreSynsets for a word in WordNet in NLP
Introduction WordNet is a large database of words present in the NLTK library in present in many languages for Natural Language related use cases. NLTK library has an interface known as Synset that allows us to look for words in WordNet. Verbs, Nouns, etc. are grouped into sunsets. WordNet and Synsets The below diagram shows the structure of WordNet. In WordNet, the relationship between words is maintained. For example, words like sad are similar and find the application under similar contexts. These words can be interchanged during usage. These kinds of words are grouped for synsets. Each synset is ...
Read MoreImpacts of Artificial Intelligence in everyday life
In today’s date, Artificial Intelligence has impacted our lives way in that nothing else could have impacted. It has changed how our daily jobs are done and has involvement in major areas of our industry, lives, and almost everything. Here in this article, we are going to see some areas in our lives where Artificial Intelligence has significantly played its role. Health Industry Healthcare is a growing and crucial industry in the 21st century. Not only it is providing a better life to people but also saving millions of lives every day. We just can't overlook the health aspect of ...
Read MoreNon-Negative Matrix Factorization
Introduction Non-Negative Matrix Factorization (NMF) is a supervised algorithm used to represent data into lower dimensions which reduces the number of features while preserving enough basic information to construct the original matrix from the reduced feature space. In this article, we will be going explore more about NMF and how it can be useful. Non-Negative Matrix Factorization NMF is used to reduce the dimensions of the input matrix or corpus. It uses factor analysis which gives less importance to less relevant words. The decomposition of the original matrix(which is a non-negative matrix) thus creates a product of two non-negative coefficients ...
Read MoreMultilingual Google Meet Summarizer and Python Project
Introduction Multilingual Google Meet summarizer is a tool/chrome extension that can create transcriptions for google meet conversations in multiple languages. During the COVID times people, they need a tool that can effectively summarize meetings, classroom lectures, and convection videos. Thus such a tool can be quite useful in this regard. In this article, let us have an overview of the project structure and explore some implementation aspects with the help of code. What this project is all about? This is a simple chrome extension that when enabled during a google meet session can generate meeting transcriptions and summarize the conversation ...
Read MoreImplementation of Teaching Learning Based Optimization
Introduction Teaching Learning Based Optimization (TLBO) is based on the relationship between a teacher and the learners in a class. In a particular class, a teacher imparts knowledge to the students through his/her hard work. The students or learners then interact with each other among themselves and improve their knowledge. Let us explore more about Teacher Learning Based Optimization through this article. What is TLBO? Let us consider a population p (particularly a class) and the number of learners l in the class. There may be decisive variables (subjects from which learners gain knowledge) for the optimization problem. Two modes ...
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