Clustering, K-nearest Neighbors and Support Vector Machines

Problem-solving tools offered by digital technology

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

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Top 8 Machine Learning Algorithms

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. K-Nearest Neighbors (KNN): This method classifies a data point based on the majority class of its K nearest neighbors in the training data.

Machine Learning

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Credit Card Fraud Detection Using Spectral Clustering

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.

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Five machine learning types to know

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

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Machine learning world easy-to-understand overview for beginners

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. It’s a fantastic world, trust me!

Machine Learning

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An Overview of Extreme Multilabel Classification (XML/XMLC)

Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. The feature space reduction is performed by aggregating clusters of features of balanced size. This clustering is usually performed using hierarchical clustering.

K-nearest Neighbors

K-nearest Neighbors Algorithm Clustering Support Vector Machines

Everything you should know about AI models

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?

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Everything you should know about AI models

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?

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Introduction to Feature Scaling in Machine Learning

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

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Journey to AI blog

DECEMBER 19, 2023

This type of machine learning is useful in known outlier detection but is not capable of discovering unknown anomalies or predicting future issues. “Means,” or average data, refers to the points in the center of the cluster that all other data is related to.

Machine Learning

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Everything to know about Anomaly Detection in Machine Learning

Pickl AI

SEPTEMBER 3, 2023

Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Points that don’t belong to any cluster or are in low-density regions are considered anomalies.

Machine Learning

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Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines.

Machine Learning

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.

Data Analyst

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance.

Data Science

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Credit Card Fraud Detection Using Spectral Clustering

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Five machine learning types to know

Machine learning world easy-to-understand overview for beginners

An Overview of Extreme Multilabel Classification (XML/XMLC)

Everything you should know about AI models

Everything you should know about AI models

Introduction to Feature Scaling in Machine Learning

Anomaly detection in machine learning: Finding outliers for optimization of business functions

Everything to know about Anomaly Detection in Machine Learning

Understanding and Building Machine Learning Models

Basic Data Science Terms Every Data Analyst Should Know

[Updated] 100+ Top Data Science Interview Questions

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