Hands-on machine learning course with Python covering supervised learning (SVM, regression), unsupervised learning (K-Means, IRIS dataset), and deep learning (CNNs) using scikit-learn and TensorFlow.

Abstract: This paper consists of an in-depth analysis of two popular supervised learning techniques: Support Vector Machine (SVM) classifier and Logistic Regression. This work aims to demonstrate the ...

Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China Background: Although an intracranial aneurysm (IA) is widespread and fatal, few drugs ...

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Microsoft Visual Studio Code is a flexible, cross-platform editor that can be transformed into a full-blown IDE for most any language or workflow. Over the past few years, it has exploded in ...

In this repository i performed a support vector regression on real life data , initially i performed some data preprocessing technique in order to filter out the data flaws then undergoes the process ...

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f.