Dataconomy

MARCH 25, 2025

Hyperplanes are pivotal fixtures in the landscape of machine learning, acting as crucial decision boundaries that help classify data into distinct categories. Their role extends beyond mere classification; they also facilitate regression and clustering, demonstrating their versatility across various algorithms.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Clustering 91

Dataconomy

MARCH 25, 2025

Learning the decision boundary Machine learning algorithms learn decision boundaries through a training process that adjusts the model’s parameters based on the input data. Algorithms like logistic regression or support vector machines focus on optimizing the decision boundary to minimize misclassification errors.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Clustering 91

Flipboard

JULY 2, 2025

To enhance predictive accuracy, machine learning models Linear Regression, Random Forest Regression, Support Vector Machines (SVM), K-Means Clustering, and Artificial Neural Networks (ANN) were employed to analyze combustion-induced degradation trends, confirming Test-06 as the optimal balance of stability and high performance.

Machine Learning 65

Machine Learning Machine Learning Support Vector Machines Clustering 65

Dataconomy

APRIL 16, 2025

Support Vector Machines: A method that finds the hyperplane separating different classes with the largest margin. Examples of unsupervised tasks include clustering and dimensionality reduction, which do not have a clear output requirement.

Supervised Learning 91

Supervised Learning Decision Trees Algorithm Machine Learning 91

How to Learn Machine Learning

MARCH 25, 2025

Some common supervised learning algorithms include decision trees, random forests, support vector machines, and linear regression. Clustering algorithms like k-means, hierarchical clustering, and density-based clustering are widely used. Unsupervised learning outputs are not as direct.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59

Dataconomy

MARCH 28, 2025

Common types include: K-means clustering: Groups similar data points together based on specific metrics. Hierarchical clustering: Creates a nested series of clusters through a tree-like structure. Nave Bayes: A straightforward classifier leveraging the independence of features.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Towards AI

FEBRUARY 23, 2024

Learn how to apply state-of-the-art clustering algorithms efficiently and boost your machine-learning skills.Image source: unsplash.com. This is called clustering. In Data Science, clustering is used to group similar instances together, discovering patterns, hidden structures, and fundamental relationships within a dataset.

Clustering 115

Clustering Support Vector Machines Machine Learning Machine Learning 115

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.

Data Analysis 370

Data Analysis Data Analysis Support Vector Machines Algorithm 370

Data Science Dojo

JULY 15, 2024

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Clustering Algorithms: Clustering algorithms can group data points with similar features. Points that don’t belong to any well-defined cluster might be anomalies.

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

Data Science Dojo

MAY 1, 2023

Scikit-learn Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines.

Python 329

Python Machine Learning Machine Learning Data Science 329

Data Science Dojo

FEBRUARY 15, 2023

Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2 1) Big O Notation 2) Search 3) Sort 3)–i)–Quicksort 3)–ii–Mergesort 4) Stack 5) Queue 6) Array 7) Hash Table 8) Graph 9) Tree (e.g.,

K-nearest Neighbors 195

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

Pickl AI

MAY 10, 2023

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. What is Classification? Hence, the assumption causes a problem.

Clustering 52

Clustering Decision Trees Machine Learning Machine Learning 52

Data Science Dojo

MAY 1, 2023

Scikit-learn Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines.

Python 195

Python Machine Learning Machine Learning Data Science 195

Towards AI

APRIL 5, 2023

SmartCore SmartCore is a machine learning library written in Rust that provides a variety of algorithms for regression, classification, clustering, and more. The library encompasses both conventional and advanced machine learning techniques, including linear regression, k-means clustering, random forests, and support vector machines.

Machine Learning 98

Machine Learning Machine Learning Support Vector Machines Clustering 98

PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Spectral clustering, a technique rooted in graph theory, offers a unique way to detect anomalies by transforming data into a graph and analyzing its spectral properties.

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57

Data Science Dojo

AUGUST 11, 2023

Supervised machine learning algorithms, such as linear regression and decision trees, are fundamental models that underpin predictive modeling. Unsupervised learning models, like clustering and dimensionality reduction, aid in uncovering hidden structures within data. Decision trees are used to classify data into different categories.

Data Science 195

Data Science Python Data Scientist Decision Trees 195

Data Science Dojo

APRIL 10, 2023

In data mining, popular algorithms include decision trees, support vector machines, and k-means clustering. This is similar as you consider many factors while you pay someone for essay , which may include referencing, evidence-based argument, cohesiveness, etc.

Data Mining 195

Data Mining Data Mining Data Mining Algorithm 195

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Towards AI

JUNE 19, 2024

. – Algorithms: Support Vector Machines (SVM), Random Forest, Neural Networks. – Algorithms: K-means Clustering, ISODATA. Use Cases: Initial data exploration, finding natural clusters in data. – Use Cases: Land cover classification, urban planning. filterBounds(aoi).median().clip(aoi);//

Algorithm 81

Algorithm Clustering Support Vector Machines Machine Learning 81

Towards AI

APRIL 14, 2023

The feature space reduction is performed by aggregating clusters of features of balanced size. This clustering is usually performed using hierarchical clustering. The idea is to sort the labels into clusters to create a meta-label space.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

AWS Machine Learning Blog

JANUARY 17, 2024

We can analyze activities by identifying stops made by the user or mobile device by clustering pings using ML models in Amazon SageMaker. A cluster of pings represents popular spots where devices gathered or stopped, such as stores or restaurants. Manually managing a DIY compute cluster is slow and expensive.

Clustering 133

Clustering AWS ML ML 133

Towards AI

FEBRUARY 20, 2024

Common Machine Learning Algorithms Machine learning algorithms are not limited to those mentioned below, but these are a few which are very common. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g.,

Machine Learning 109

Machine Learning Machine Learning ML ML 109

Pickl AI

AUGUST 1, 2023

Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. Common examples include: Linear Regression: It is the best Machine Learning model and is used for predicting continuous numerical values based on input features.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Mlearning.ai

FEBRUARY 17, 2023

Logistic Regression K-Nearest Neighbors (K-NN) Support Vector Machine (SVM) Kernel SVM Naive Bayes Decision Tree Classification Random Forest Classification I will not go too deep about these algorithms in this article, but it’s worth it for you to do it yourself. Great example of this tecnique is K-means clustering algorithm.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

IBM Journey to AI blog

DECEMBER 19, 2023

Machine learning algorithms for unstructured data include: K-means: This algorithm is a data visualization technique that processes data points through a mathematical equation with the intention of clustering similar data points. Isolation forest models can be found on the free machine learning library for Python, scikit-learn.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Dataconomy

AUGUST 31, 2023

The goal of unsupervised learning is to identify structures in the data, such as clusters, dimensions, or anomalies, without prior knowledge of the expected output. Some popular classification algorithms include logistic regression, decision trees, random forests, support vector machines (SVMs), and neural networks.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Pickl AI

AUGUST 1, 2023

It helps in discovering hidden patterns and organizing text data into meaningful clusters. Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. within the text.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Mlearning.ai

APRIL 8, 2023

The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Support vector machine (SVM) based models. Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM ” by Deepak Narayanan et al.

Natural Language Processing 98

Natural Language Processing Algorithm Machine Learning Machine Learning 98

Dataconomy

MAY 25, 2023

Supervised learning algorithms, like decision trees, support vector machines, or neural networks, enable IoT devices to learn from historical data and make accurate predictions. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Dataconomy

AUGUST 9, 2023

It constructs multiple decision trees and combines their predictions to achieve accurate results in identifying different types of network traffic Support Vector Machines (SVM) : SVM is used for both classification and anomaly detection.

Machine Learning 91

Machine Learning Machine Learning ML ML 91

Pickl AI

NOVEMBER 20, 2024

Practical Applications of Linear Algebra in Machine Learning Discover the practical applications of Linear Algebra in Machine Learning, including data preprocessing, model training, dimensionality reduction, and clustering. Model Training Most Machine Learning models rely heavily on Linear Algebra during training phases.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Clustering 52

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. How do you handle missing values in a dataset?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

AUGUST 6, 2024

Clustering Algorithms Techniques such as K-means clustering can help identify groups of similar data points. Points that do not belong to any cluster may be considered anomalies. Support Vector Machines (SVM) SVM can be employed for anomaly detection by finding the hyperplane that best separates normal data from anomalies.

Data Quality 52

Data Quality Clustering Support Vector Machines Algorithm 52

Pickl AI

JULY 12, 2024

Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. classification, regression) and data characteristics.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

AUGUST 22, 2023

This harmonization is particularly critical in algorithms such as k-Nearest Neighbors and Support Vector Machines, where distances dictate decisions. Scaling steps in as a guardian, harmonizing the scales and ensuring that algorithms treat each feature fairly.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 52