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Machine Learning Articles
Page 44 of 56
Non-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 MoreImplementation of Particle Swarm Optimization
Introduction The Particle Swarm Optimization algorithm is inspired by nature and is based on the social behavior of birds in a flock or the behavior of fish and is a population-based algorithm for search. It is a simulation to discover the pattern in which birds fly and their formations and grouping during flying activity. Particle Swarm Optimization Algorithm In the PSO Algorithm, each individual is considered to be a particle in some high-dimensional search space. Inspired by the social and psychological behavior of people, which they tend to copy from other people's success, similar changes are made to the particles ...
Read MoreHow to Become an RPA Developer?
Introduction RPA stands for Robotic Process Automation. An RPA developer is a person who designs, maintains, builds, and implements RPA systems. In an organization, an RPA developer has the role to create optimized workflow processes and work cross-functionally with operations and business analysts. Scope of an RPA Developer Today the world is moving towards automation where organizations maximum repetitive processes to be automated as much as possible. Thus the demand for highly skilled RPA professionals has gained pace. With the right skill set, RPA developers can fill the in the respective domains. An RPA developer is essentially a software developer ...
Read MoreExploratory Data Analysis (EDA) - Types and Tools
Introduction Exploratory Data Analysis (EDA) is the process of summarization of a dataset by analyzing it. It is used to investigate a dataset and lay down its characteristics. EDA is a fundamental process in many Data Science or Analysis tasks. Different types of Exploratory Data Analysis There are broadly two categories of EDA Univariate Exploratory Data Analysis – In Univariate Data Analysis we use one variable or feature to determine the characteristics of the dataset. We derive the relationships and distribution of data concerning only one feature or variable. In this category, we have the liberty to use either ...
Read MoreDocument Retrieval using Boolean Model and Vector Space Model
Introduction Document Retrieval in Machine Learning is part of a larger aspect known as Information Retrieval, where a given query by the user, the system tries to find relevant documents to the search query as well as rank them in order of relevance or match. They are different ways of Document retrieval, two popular ones are − Boolean Model Vector Space Model Let us have a brief understanding of each of the above methods. Boolean Model It is a set-based retrieval model.The user query is in boolean form. Queries are joined using AND, OR, NOT, etc. A document ...
Read MoreDeepWalk Algorithm
Introduction The graph is a very useful data structure that can represent co-interactions. These co-interactions can be encoded by neural networks as embeddings to be used in different ML Algorithms. This is where the DeepWalk algorithm shines. In this article, we are going to explore the DeepWalk algorithm with a Word2Vec example. Let us learn more about Graph Networks on which the core of this algorithm is based. The Graph If we consider a particular ecosystem, a graph generally represents the interaction between two or more entities. A Graph Network has two objects – node or vertex and edge. ...
Read MoreDeep Learning and the Internet of Things
Deep Learning provides a new horizon to the Internet of Things. The availability of different IoT sensors helps us collect data that is so important for Deep Learning. There are many uses of Deep Learning in IoT, which makes IoT more powerful. Deep Learning and the Internet of Things together form a new era that is more advanced than Web 3.0. IoT Divisions The major key sub-divisions under IoT, like IoP (Internet of People), IoT (Internet of Everything), and IIoT (Industrial Internet of Things), are developing and highly dependent upon Deep Learning technology. There are many different Deep Learning Models ...
Read MoreMobile development vs Machine Learning: Best Career Options
Introduction Two of the most promising careers in technology today are mobile development and machine learning. Professionals who are interested in developing novel solutions and pushing the limits of what is conceivable in the technological world will find intriguing prospects in both of these disciplines. Yet, choosing a professional route can be challenging for many people because each choice has its own distinct benefits and drawbacks. In order to assist you to choose which job path is ideal for you, we will examine the advantages and disadvantages of pursuing careers in mobile development and machine learning in this post. Mobile ...
Read MoreWhy should you learn machine learning and artificial intelligence
Introduction Due to the rising need for qualified individuals, interesting job prospects, commercial applications, customization, and innovation, studying machine learning (ML) and artificial intelligence (AI) is becoming more and more crucial. Professionals who can design, construct, and maintain these systems are required as more businesses use AI and ML technology. In addition to providing interesting job prospects across a range of industries, ML and AI may assist organizations in streamlining operations, making data-driven choices, and increasing productivity and profitability. Moreover, ML and AI are at the forefront of technological advancement and may be utilized to tailor client experiences. People can ...
Read MoreWhat is Overfitting and how to avoid it?
Introduction In statistics, the phrase "overfitting" is used to describe a modeling error that happens when a function correlates too tightly to a certain set of data. As a result, overfitting could not be able to fit new data, which could reduce the precision of forecasting future observations. Examining validation measures like accuracy and loss might show overfitting. The validation measures frequently increase until a point at which they level out or start to drop when the model is affected by overfitting. During an upward trend, the model looks for a good match, and once it finds one, the movement ...
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