Machine Learning (ML)

12 benefits of using Machine Learning in healthcare

Machine learning is a potent tool that has transformed several industries, including healthcare. Machine learning may assist in the analysis of huge amounts of data, the identification of patterns and trends, and the prediction of outcomes based on that data.

Decision Region

The boundary that distinguishes one class from another in a classification issue is known as a decision region in machine learning. It is the region of the input space that translates to a particular output or class.

Machine Learning (ML)

Multi-output learning and Multi-output CNN models

In this article at OpenGenus, we have explored the part of Deep Learning where a model is trained to produce multi-outputs (more than 1) in contrast to standard Deep Learning models like ResNet50 for Image Recognition.

Machine Learning (ML)

Unpooling operations in ML models

In machine learning models, the pooling operation that was previously carried out in the network is reversed using the unpooling operation.

Machine Learning (ML)

Gradient Boosting Machines (GBM)

What is a Gradient Boosting Machine in ML? That is the first question that needs to be answered to a beginner to Machine Learning.

Machine Learning (ML)

30 Data Mining Projects [with source code]

In this article at OpenGenus, we will explore some of the most interesting and innovative data mining project ideas that have been undertaken in recent years.

Machine Learning (ML)

Machine Learning for Software Engineering

In this article, we explain machine learning, software engineering and how machine learning can be used for software engineering.

Machine Learning (ML)

Decision Boundary in ML

Decision boundary is a crucial concept in machine learning and pattern recognition. It refers to the boundary or surface that separates different classes or categories in a classification problem.

Machine Learning (ML)

Data Sampling and Data Splitting in ML

In this article, we will explore the concept of Data Sampling and Data Splitting in Machine Learning.

Machine Learning (ML)

Bias Variance tradeoff

An essential idea in statistical learning and machine learning is the bias-variance tradeoff. It speaks to the connection between a model's complexity and its precision in fitting the data.

Machine Learning (ML)

Gibbs Sampling

Gibbs Sampling is a statistical method for obtaining a sequence of samples from a multivariate probability distribution.

Machine Learning (ML)

3 Types of Logistic Regression

In this article, we have explained the basic concept of Logistic Regression and presented the 3 different types of Logistic Regression.

Machine Learning (ML)

Imbalanced Data in ML

When a dataset's distribution of classes is uneven, it is said to have imbalanced data. In other words, compared to the other classes, one class has significantly more or fewer samples.

Artificial Intelligence

The Future of Artificial Intelligence and its potential impact on society

Over the past ten years, artificial intelligence (AI) has made considerable strides, and its potential impact on society is enormous. We shall talk about the future of AI and possible societal repercussions in this essay.

Machine Learning (ML)

Region of Interest and ROI Pooling

In computer vision and image processing, region of interest (ROI) and ROI pooling are crucial ideas. In typical operations like object identification, segmentation, and tracking.

Machine Learning (ML)

Exploration-Exploitation Dilemma

The exploration-exploitation dilemma is a concept that describes the challenge of deciding between exploring new options or exploiting already known options to maximize rewards.

Machine Learning (ML)

9 Advantages and 10 disadvantages of Naive Bayes Algorithm

In this article, we'll talk about some of the key advantages and disadvantages of Naive Bayes algorithm.

Machine Learning (ML)

CHAID in ML

In this article, we have explored the concept of CHAID, or Chi-Squared Automatic Interaction Detector in Machine Learning. This is a core concept in Decision Tree.

Machine Learning (ML)

AUC (Area Under The Curve) and ROC (Receiver Operating Characteristics)

In binary classification issues, the metrics AUC (Area Under the Curve) and ROC (Receiver Operating Characteristics) are frequently employed. AUC is a numerical metric that measures the performance of the classifier, whereas ROC is a graphical plot that shows the performance of a binary classifier.

Machine Learning (ML)

3 Types of Naive Bayes

In this article, we have explored the 3 different types of Naive Bayes classification algorithm in depth.

Deep Learning

Markov Chain in Neural Network

In this article, we have explored the concept of Markov chain along with their definition, applications, and operational details. We have covered how Markov Chain is used in the field of Deep Learning/ Neural Network.

Machine Learning (ML)

Feature, vector and embedding space

In this article, we will discuss the concepts of feature, vector, and embedding space and their importance in machine learning.

Machine Learning (ML)

4 Types of Machine Learning

In this article, we have explored the 4 different types of Machine Learning (ML). The goal of ML is to create techniques so that computers can act close to human behavior and the current techniques fall into 4 distinct categories.

Deep Learning

Training, Testing, Validation and Holdout Set

Training, testing, validation, and holdout sets are essential components of machine learning models that allow for effective evaluation of model performance and generalization. In this article, we will delve into what these sets are, how they are used, and why they are important.

Deep Learning

Epoch, Iteration and Batch in Deep Learning

In this article, we will explore three fundamental concepts in deep learning: epoch, iteration, and batch. These concepts are essential in training deep neural networks and improving their accuracy and performance.