KDnuggets

SEPTEMBER 12, 2019

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

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Analytics Vidhya

OCTOBER 6, 2021

This article was published as a part of the Data Science Blogathon Introduction Hello Everyone, I hope you are doing well. 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 […].

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Analytics Vidhya

OCTOBER 23, 2020

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

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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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Support Vector Machines Data Science Analytics Analytics 291

Analytics Vidhya

JULY 5, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Source Overview In this article, we will learn the working of. The post Start Learning SVM (Support Vector Machine) Algorithm Here! appeared first on Analytics Vidhya.

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Support Vector Machines Algorithm Data Science Analytics 271

Analytics Vidhya

APRIL 26, 2021

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

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Support Vector Machines Python Data Science Analytics 271

Dataconomy

APRIL 17, 2025

Support vector machines (SVM) are at the forefront of machine learning techniques used for both classification and regression tasks. This article delves into the essential components of SVM and its advantages and disadvantages, providing a comprehensive overview of its functionalities and challenges.

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Analytics Vidhya

OCTOBER 16, 2022

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

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Support Vector Machines Algorithm Machine Learning Machine Learning 394

Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Before the sudden rise of neural networks, Support Vector Machines. The post Top 15 Questions to Test your Data Science Skills on SVM appeared first on Analytics Vidhya.

Data Science 229

Data Science Support Vector Machines Analytics Analytics 229

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. These are often used to infer causal relationships between variables.

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Mlearning.ai

MAY 12, 2023

Support Vector Machine: A Comprehensive Guide — Part1 Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. I will cover only the first 5 subtopics in this article and will cover the rest in my next upcoming article.

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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.

Python 327

Python Machine Learning Machine Learning Data Science 327

Mlearning.ai

MAY 22, 2023

Support Vector Machine: A Comprehensive Guide — Part2 In my last article, we discussed SVMs, the geometric intuition behind SVMs, and also Soft and Hard margins. Transformed Data into 2-D Data Conclusion Support Vector Machines (SVMs) offer a powerful framework for classification and regression tasks.

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Analytics Vidhya

AUGUST 25, 2024

In this article, we’ll dive […] The post Are You Making These Common Mistakes in Classification Modeling? Imagine building a cutting-edge model that dazzles with high accuracy, only to find it crumbles under real-world pressure. appeared first on Analytics Vidhya.

Machine Learning 290

Machine Learning Machine Learning Analytics Analytics 290

Data Science Dojo

FEBRUARY 15, 2023

as described via the relevant Wikipedia article here: [link] ) and other factors, the digital age will keep producing hardware and software tools that are both wondrous, and/or overwhelming (e.g., For instance, in the table below, we juxtapose four authors’ professional opinions with DS-Dojo’s curriculum. IoT, Web 3.0,

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K-nearest Neighbors Decision Trees Support Vector Machines Data Science 195

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.

Python 195

Python Machine Learning Machine Learning Data Science 195

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.

Machine Learning 98

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Dataconomy

AUGUST 28, 2023

How to use kernels in machine learning Kernels, the unsung heroes of AI and machine learning, wield their transformative magic through algorithms like Support Vector Machines (SVM).

Machine Learning 172

Machine Learning Machine Learning Algorithm Support Vector Machines 172

Pickl AI

AUGUST 8, 2024

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Among the many algorithms, the SVM algorithm in Machine Learning stands out for its accuracy and effectiveness in classification tasks. What is the SVM Algorithm in Machine Learning?

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Smart Data Collective

JUNE 7, 2019

Machine learning is playing a very important role in improving the functionality of task management applications. In January, Towards Data Science published an article on this very topic. “In Both of these types of learning are used by machine learning algorithms in modern task management applications. Logistic Regression.

Machine Learning 101

Machine Learning Machine Learning Supervised Learning Support Vector Machines 101

Towards AI

JULY 15, 2024

R has simplified the most complex task of geospatial machine learning and data science. Hopefully, this article will serve as a roadmap for leveraging the power of R, a versatile programming language, for spatial analysis, data science and visualization within GIS contexts. data = trainData) 5.

Machine Learning 116

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Towards AI

APRIL 27, 2023

In this article, we will provide an overview of gradient descent, including the mathematical concepts behind it, the different variants of gradient descent, and the challenges and limitations associated with it. Recommender Systems: In recommender systems, gradient descent is used to optimize […]

Machine Learning 97

Machine Learning Machine Learning Support Vector Machines Algorithm 97

Pickl AI

JANUARY 12, 2025

Summary: The article explores the differences between data driven and AI driven practices. Develop Hybrid Models Combine traditional analytical methods with modern algorithms such as decision trees, neural networks, and support vector machines.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

ODSC - Open Data Science

FEBRUARY 13, 2023

Machine Learning for Beginners Learn the essentials of machine learning including how Support Vector Machines, Naive Bayesian Classifiers, and Upper Confidence Bound algorithms work. After this talk, you will have an intuitive understanding of these three algorithms and real-life problems where they can be applied.

Machine Learning 98

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Towards AI

APRIL 11, 2023

Images used in my articles are Properties of the Respective Organisations and are used here solely for Reference, Illustrative and Educational Purposes Only. Hand-Written Digits This problem is a simple example of pattern recognition and is widely used in Image Processing and Machine Learning.

Machine Learning 98

Machine Learning Machine Learning Support Vector Machines Algorithm 98

Towards AI

JULY 20, 2023

This includes one paper from 2020 that conducted feature extraction using a denoising autoencoder alongside a deep neural network, and a flattened vector and support vector machines to evaluate study relevance. used ML and NLP to generate automatic summaries of full-text articles, achieving high rates of recall (91.2%

Natural Language Processing 96

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. Evolution of SOTA models in NLP and factors affecting them Here is the evolutionary map for this article.

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Towards AI

JANUARY 26, 2024

This article will explain the concept of hyperparameter tuning and the different methods that are used to perform this tuning, and their implementation using python Photo by Denisse Leon on Unsplash Table of Content Model Parameters Vs Model Hyperparameters What is hyperparameter tuning? C can take any positive float value.

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K-nearest Neighbors Deep Learning Deep Learning Algorithm 100

Towards AI

APRIL 14, 2023

Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels. In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations.

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Towards AI

JUNE 19, 2024

In this article, I will show you what algorithm to use for each purpose and attain the desirable outcome you want without spending time going through each algorithm trying to figure out which one you want. – Algorithms: Support Vector Machines (SVM), Random Forest, Neural Networks.

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Mlearning.ai

MARCH 13, 2023

You can start with simpler algorithms such as decision trees, Naive Bayes , and logistic regression, and steadily move to more complex ones like neural networks and support vector machines (SVM). If this article has been able to add to your knowledge, please share it with your colleagues and link it to your stories.

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Mlearning.ai

APRIL 6, 2023

In this article, we will discuss some of the factors to consider while selecting a classification & Regression machine learning algorithm based on the characteristics of the data. For larger datasets, more complex algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks may be more suitable.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Dataconomy

AUGUST 31, 2023

Some popular classification algorithms include logistic regression, decision trees, random forests, support vector machines (SVMs), and neural networks. Choose a suitable classification algorithm based on the type of classification problem and the data.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Chatbots Life

APRIL 16, 2024

In this article, we will delve into the concepts of generative and discriminative models, exploring their definitions, working principles, and applications. Support Vector Machines (SVM): SVM finds an optimal hyperplane to separate different classes in high-dimensional spaces. What are some popular discriminative models?

Natural Language Processing 59

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 59

Becoming Human

MARCH 16, 2023

In this article, we will explore how AI drug discovery is changing the industry. Hence, the interest in finding new ways we discover and design drugs. AI has already helped identify promising candidate therapeutics, and it didn’t take years but months or even days. We will look at success stories, AI benefits, and limitations.

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Heartbeat

SEPTEMBER 28, 2023

To address this challenge, data scientists harness the power of machine learning to predict customer churn and develop strategies for customer retention. Model Training We train multiple machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

Machine Learning 52

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