Data Science Dojo

FEBRUARY 14, 2024

Normalization A feature scaling technique is often applied as part of data preparation for machine learning.

K-nearest Neighbors 243

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 243

Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

PyImageSearch

SEPTEMBER 16, 2024

Supervised Learning These methods require labeled data to train the model. The model learns to distinguish between normal and abnormal data points. For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. Ethical considerations are crucial in developing fair Machine Learning solutions.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Dataconomy

MARCH 28, 2025

Specific types of machine learning algorithms Among the several algorithms available, some notable types include: Support vector machine (SVM): Ideal for binary classification tasks. K-nearest neighbors (KNN): Classifies based on proximity to other data points.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91