Analytics Vidhya

NOVEMBER 27, 2020

The post Understanding Naïve Bayes and Support Vector Machine and their implementation in Python appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction In this digital world, spam is the most troublesome challenge that.

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Analytics Vidhya

OCTOBER 6, 2021

Ever wondered, how great would it be, if we could predict, whether our request for a loan, will be approved or not, simply by the use of machine learning, from the ease and comfort […]. The post Loan Status Prediction using Support Vector Machine (SVM) Algorithm appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 26, 2020

Unlocking a New World with the Support Vector Regression Algorithm Support Vector Machines (SVM) are popularly and widely used for classification problems in machine. The post Support Vector Regression Tutorial for Machine Learning appeared first on Analytics Vidhya.

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KDnuggets

SEPTEMBER 12, 2019

This article explains the Support Vector Machines (SVM) algorithm in an easy way.

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Analytics Vidhya

OCTOBER 23, 2020

Introduction One of the classifiers that we come across while learning about. The post The Mathematics Behind Support Vector Machine Algorithm (SVM) appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

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Support Vector Machines Algorithm Data Science Analytics 298

Analytics Vidhya

JULY 9, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction A Support Vector Machine (SVM) is a very powerful and. The post Support Vector Machine and Principal Component Analysis Tutorial for Beginners appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 8, 2021

ArticleVideo Book Objective Learn how the support vector machine works Understand the role and types of kernel functions used in an SVM. The post Beginner’s Guide to Support Vector Machine(SVM) appeared first on Analytics Vidhya. Introduction.

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Support Vector Machines Analytics Analytics Machine Learning 250

KDnuggets

AUGUST 23, 2022

This post focuses on building an intuition of the Support Vector Machine algorithm in a classification context and an in-depth understanding of how that graphical intuition can be mathematically represented in the form of a loss function. We will also discuss kernel tricks and a more useful variant of SVM with a soft margin.

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Dataconomy

APRIL 17, 2025

Support vector machines (SVM) are at the forefront of machine learning techniques used for both classification and regression tasks. What are support vector machines (SVMs)? Advantages of support vector machines SVMs offer several advantages, particularly in terms of accuracy and efficiency.

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Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a cornerstone of machine learning, providing powerful techniques for classifying and predicting outcomes in complex datasets. What are Support Vector Machines (SVM)? They work by identifying a hyperplane that best separates distinct classes within the data.

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Analytics Vidhya

APRIL 26, 2021

The post Introduction to SVM(Support Vector Machine) Along with Python Code appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction This article aims to provide a basic understanding.

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Support Vector Machines Python Data Science Analytics 271

KDnuggets

JULY 10, 2023

A guide to understanding support vector machines for classification: from theory to scikit-learn implementation.

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Support Vector Machines Machine Learning Machine Learning 237

Data Science Dojo

MAY 24, 2023

Machine learning practices are the guiding principles that transform raw data into powerful insights. By following best practices in algorithm selection, data preprocessing, model evaluation, and deployment, we unlock the true potential of machine learning and pave the way for innovation and success.

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Flipboard

NOVEMBER 28, 2023

In a previous tutorial, we have explored the use of the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques that comes implemented in the OpenCV library.

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Analytics Vidhya

OCTOBER 16, 2022

Introduction Support vector machine is one of the most famous and decorated machine learning algorithms in classification problems. This article was published as a part of the Data Science Blogathon.

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Data Science Dojo

JULY 15, 2024

By understanding machine learning algorithms, you can appreciate the power of this technology and how it’s changing the world around you! Predict traffic jams by learning patterns in historical traffic data. Learn in detail about machine learning algorithms 2.

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KDnuggets

AUGUST 7, 2019

There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.

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Flipboard

MAY 31, 2025

Negative academic emotions reflect the negative experiences that learners encounter during the learning process. This study validates the applicability and value of machine learning models in predicting negative academic emotions, providing important insights for educational practice.

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Analytics Vidhya

MARCH 28, 2024

Introduction The One-Class Support Vector Machine (SVM) is a variant of the traditional SVM. It is specifically tailored to detect anomalies. Its primary aim is to locate instances that notably deviate from the standard.

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Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. Introduction Machine Learning algorithms are transforming the way we interact with technology, making it possible for systems to learn from data and improve over time without explicit programming.

Machine Learning 52

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Data Science Dojo

MAY 1, 2023

10 Python packages for data science and machine learning In this article, we will highlight some of the top Python packages for data science that aspiring and practicing data scientists should consider adding to their toolbox. Scikit-learn Scikit-learn is a powerful library for machine learning in Python.

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Python Machine Learning Machine Learning Data Science 327

Pickl AI

APRIL 6, 2025

Summary: Kernel methods in machine learning solve complex data problems using smart functions like the kernel trick. Learn how they work and how to apply them in real-world projects through Pickl.AIs data science courses. Learn how they work and how to apply them in real-world projects through Pickl.AIs data science courses.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Data Science 52

Dataconomy

APRIL 8, 2025

Among the most significant models are non-linear models, support vector machines, and linear regression. Support vector machines (SVM) Support Vector Machines are a robust classification technique in machine learning.

Data Science 91

Data Science Support Vector Machines Data Scientist Data Visualization 91

Dataconomy

AUGUST 31, 2023

Classification in machine learning involves the intriguing process of assigning labels to new data based on patterns learned from training examples. Machine learning models have already started to take up a lot of space in our lives, even if we are not consciously aware of it. 0 or 1, yes or no, etc.).

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Data Science Dojo

FEBRUARY 14, 2024

These features can be used to improve the performance of Machine Learning Algorithms. In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results.

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Dataconomy

APRIL 29, 2025

Binary classification plays a pivotal role in the world of machine learning, allowing for the division of data into two distinct categories. Overview of classification in machine learning Classification serves as a foundational method in machine learning, where algorithms are trained on labeled datasets to make predictions.

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Towards AI

APRIL 5, 2023

Rustic Learning: Machine Learning in Rust — Part 2: Regression and Classification An Introduction to Rust’s Machine Learning crates Photo by Malik Skydsgaard on Unsplash Rustic Learning is a series of articles that explores the use of Rust programming language for machine learning tasks.

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Towards AI

JULY 15, 2024

R has become ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation. R has simplified the most complex task of geospatial machine learning and data science. Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. data = trainData) 5.

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Analytics Vidhya

AUGUST 25, 2024

Introduction Assessing a machine learning model isn’t just the final step—it’s the keystone of success. Imagine building a cutting-edge model that dazzles with high accuracy, only to find it crumbles under real-world pressure.

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Data Science Dojo

FEBRUARY 7, 2023

They are also used in machine learning, such as support vector machines and k-means clustering. Robust inference: Robust inference is a technique that is used to make inferences that are not sensitive to outliers or extreme observations.

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Data Analysis Data Analysis Support Vector Machines Algorithm 370

Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. This approach involves techniques where the machine learns from massive amounts of data.

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Dataconomy

MARCH 6, 2025

Common types of surrogate models Surrogate modeling encompasses various machine learning methodologies, including: Polynomial regressions: Useful for capturing relationships in a straightforward manner. Support vector machines: Effective in high-dimensional spaces and can handle nonlinearities.

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Machine Learning Machine Learning Support Vector Machines Data Science 91

Flipboard

MAY 25, 2025

This study integrates machine learning with anomaly detection frameworks to enhance wireless network security. To validate the effectiveness of this framework, multiple machine learning algorithms based on traditional classifiers which are compared for their ability to detect anomalies, particularly jamming attacks.

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Flipboard

MAY 30, 2025

Moreover, existing machine learning studies have limitations in integrating hydrological principles and explaining model applicability. This study focuses on this region, constructing a one-dimensional hydrodynamic model based on Saint-Venant’s equations to simulate flood-tide evolution.

Machine Learning 78

Machine Learning Machine Learning Support Vector Machines 78

Flipboard

MAY 25, 2025

This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusing on those with concomitant traumatic brain injury. The analysis included 218 patients admitted to Qilu Hospital of Shandong University from July 2011 to April 2024.

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K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 78

Towards AI

APRIL 27, 2023

Understanding the Principles, Challenges, and Applications of Gradient Descent Image by Author with @MidJourney Introduction to Gradient Descent Gradient descent is a fundamental optimization algorithm used in machine learning and data science to find the optimal values of the parameters in a model.

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