Found 664 Articles for Machine Learning

In machine learning, clustering is a fundamental method that is crucial for extracting knowledge from datasets and spotting hidden patterns. Clustering techniques let us search through massive volumes of data and find significant structures by putting related data points together. This procedure helps with data exploration, segmentation, and comprehension of intricate connections between data pieces. We can extract important insights from unlabeled data by autonomously locating clusters without the requirement for predetermined labels. Customer segmentation, anomaly detection, picture and document organization, and genomics research are just a few of the real−world applications where clustering is crucial. We'll be looking closely ... Read More

Consider the capability of machines to comprehend and traverse the complexity of geometric structures, places, and forms. This is where the intriguing fusion of geometry and machine learning is put to use. A subfield of artificial intelligence called machine learning enables computers to identify patterns and make predictions based on data. However, geometry, a fundamental branch of mathematics, deals with the properties and relationships of shapes and space. By integrating these two fields, we create a whole new world of possibilities. This article will look at the fascinating relationship between geometry and machine learning. Understanding Geometry The field of ... Read More

Introduction Machine learning's influence in IT and other industries is expanding rapidly. Despite still being in its early stages, Machine Learning has gained a lot of attention across industries. It's the study of how to program computers to learn and improve on their own. Therefore, Machine Learning is concerned with improving computer programs by utilizing data gathered from a wide range of observations. In this article, titled "Aspects of Machine Learning process, " we will explore some of the foundational ideas behind Machine Learning, including its definition, the technologies and algorithms it employs, its potential applications and examples, and more. ... Read More

Introduction The ratio of accurate predictions to inaccurate predictions is plotted in a matrix known as a confusion matrix. This would refer to the ratio of true negatives and true positives (right predictions) to false negatives and false positives for a binary classifier (incorrect predictions). After data cleaning, preprocessing, and parsing, the first thing we do is feed the data to an efficient model, which naturally produces results in probabilities. Hold on though! But how do we assess the performance of our model? Higher performance, better effectiveness—exactly that's what we want. And here’s when the Confusion matrix comes into ... Read More

Introduction In machine learning, computer vision is a field where image datasets are used and analyzed to perform several complex tasks related to the same. Here different algorithms and techniques are used related to handling and analyzing the images in order to use the data and train high-performing models. Convolution is a very important term or a phenomenon that occurs in the name of Convolutional neural networks, which is the most famous technique used for handling and dealing with image datasets in machine learning. In this article, we will discuss convolution, what are convolutional operations, and other important ... Read More

Introduction In machine learning and data science, many statistical tests are used to compare and find the differences between variables or the features of the data. These tests are mainly hypothesis tests where the conditions are defined, and according to the different tests being conducted, the relationship between variables is assumed. The t-test is also a type of statistical test that is used to compare the means of different groups of the categorical variable. In this article, we will discuss the paired t-test, which is an extension or a type of t-test used in statistics, and we will ... Read More

Introduction Time series is one of the widely used in machine learning and data science, used to forecast and analyze the data collected with time components. It is a field of intelligence where the data can be forecasted and analyzed with the help of past data collected. In industry, businesses are using the time series analyzed and related methods to improvise their decision-making process. In this article, we will discuss the ways in which the time series can help improve the decision-making process in the industry and how businesses are using the same to enhance their productivity and ... Read More

Introduction In deep learning, computer vision is one of the most important fields which is used for many complex and advanced tasks related to image datasets. It is used for image analysis, object detection, segmentations, etc. This is mainly achieved with the combination of TensorFlow and Keras, which offers several inbuilt functions which automate and make the process of model training very easy. The Conv2D is also one of the most useful and powerful functions in the Keras library, which is used for applying convolutional operations to the image. In this article, we will discuss the Conv2D function ... Read More

Introduction Padding is one of the most used concepts in neural networks while working with convolutional neural networks. It is a most known concept to every neural network engineer and deep learning engineer to efficiently extract useful information from the given dataset in deep learning. In this article, we will discuss padding, what it is, why we need padding in neural networks, what is the significance of the same, and how we can apply padding in neural networks with code examples. This article will help one to understand padding from scratch and apply it when necessary. What is ... Read More

Introduction In machine learning, classification problems are one of the most widely seen problems, where machine learning models are built to classify several categories of the target variables. However, the classification report and confusion matrics are used in order to evaluate the performance of the model and to check where the model is making mistakes. In this article, we will discuss the classification report and confusion matrics, what they are, how we can use them, and what their interpretation is by calculating the same code examples in Python. This article will help one to clear an idea about ... Read More