Found 664 Articles for Machine Learning

In the present era, where data plays a pivotal role, businesses and organizations of all sizes encounter a substantial volume of data. However, ensuring the accuracy and reliability of this data is vital for making well-informed decisions and extracting meaningful information. Data cleaning or data scrubbing, involves the identification and rectification or elimination of errors, inconsistencies, and inaccuracies within datasets. Although data cleaning holds great importance, it presents numerous challenges that can impede the accuracy and dependability of the purified data. In this article, we will explore the diverse set of challenges and issues that arise during the data cleaning ... Read More

R packages play an important role in enabling researchers, analysts, and developers to leverage the potential of machine learning in the dynamic field of data science. These programs offer a comprehensive collection of tools and functionalities that ease difficult data analysis processes, making them indispensable for industry experts. In this article, we will explore the top seven R packages for machine learning, their importance, and how to use them effectively. 7 Best R Packages for Machine Learning Below are the seven R packages for machine learning − Caret Caret is an R package that supports a wide range of machine-learning ... Read More

Machine Learning is a field of study of smart and efficient use of data and existing algorithms to make machines imitate humans in performing tasks with greater accuracy. It is one of the fastest growing branches in Computer Science , along with Artificial Intelligence. Machine Learning can be rightly called as a subset of artificial intelligence, as a lot of ML algorithms and results are of AI . Automation of Routine Tasks We are living in a fast moving world where people have no time to carry out their regular chores manually. Nearly half of the population ... Read More

ANN or artificial neural networks are the computing systems developed by taking inspiration from the biological neural networks; the human brain being its major unit. These neural networks are made functional with the help of training that follows some kind of a learning rule. A learning rule in ANN is nothing but a set of instructions or a mathematical formula that helps in reinforcing a pattern, thus improving the efficiency of a neural network. There are 6 such learning rules that are widely used by neural networks for training. Hebbian Learning Rule Developed by Donald Hebb in 1949, ... Read More

Intelligence can be defined as the ability to learn, understand and apply knowledge and skills. Human Intelligence is a multifaceted concept that encompasses many types. Before understanding the types of human intelligence let’s have a look at the basic difference between Human intelligence and Artificial intelligence − Human intelligence is associated with creativity, emotion, flexibility, and the ability to learn from a wide range of experiences. Unlike human intelligence, artificial intelligence cannot think creatively, experience emotions, adapt to new circumstances, or understand ethical and moral issues. While machine learning and human learning share some similarities, reinforcement learning uses ... Read More

A transfer neural network is a deep learning architecture that handles long-range dependencies well, as was first described in Vaswani et al's 2017 paper "All you need is attention.". The self-attention mechanism of transformer networks allows them to identify relevant parts of input sequences. What are Recurrent Neural Networks? Recurrent neural networks are artificial neural networks that have memory or feedback loops. They are designed to process and classify sequential data in which the order of the data points is important. The network works by feeding the input data into a hidden layer, allowing the network to maintain information ... Read More

PyTorch, an open-source machine learning framework, is widely used in academic research and industries. It offers ample tools and libraries to build and train neural networks effectively. Tensors in PyTorch In PyTorch, a tensor is a multidimensional array of values that can be used to represent data for machine learning models. Dimensions may vary as 1D, 2D, 3D, and so on, totally depending on the complexity of the data they represent. Let's take an example, a 1D tensor can be used to represent a sequence of values, such as time-series data, whereas a 2D tensor can be used to represent ... Read More

Machine learning uses the mathematical approach of Singular value decomposition to comprehend huge and complicated data sets. In this mathematical approach, a Unique Valued matrix A is factorized into three matrices via decomposition. In terms of the components of A, the Singular value decomposition of matrix A can be written as A=UDVT. In this case, S denotes A's singular values, whereas U and V stand for A's left and right singular vectors, respectively. Mathematical Algorithm Given Matrix A find the Transpose of matrix A that is (AT). Find A*AT Find the Eigen Vector of A*AT ... Read More

Support Vector Machine (SVM) is a supervised algorithm used widely in handwriting recognition, sentiment analysis and many more. To separate different classes, SVM calculates the optimal hyperplane that more or less accurately creates a margin between the two classes. Here are a few ways to separate hyperplanes in SVM. Data Processing − SVM requires data that is normalised, scaled and centred since they are sensitive to such features. Choose a Kernel − A kernel function is used to transform the input into a higher dimensional space. Some of them include linear, polynomial and radial base functions. Let ... Read More

"Artificial intelligence" (AI) is a branch of computer science that tries to give computers the ability to comprehend spoken and written words similar to human beings which is a field of "natural language processing" (NLP). Computational linguistics combines a variety of technologies, including deep learning, machine learning, and statistics. Combining these technologies enables computers to completely "understand" the meaning of texts and speech, including the speaker's or writer's intention and sentiment, and to interpret human language as text and audio data. Why is it Important to use Grammar Structure in NLP? Communication is the act of sharing information through signals ... Read More