Iguazio

JUNE 25, 2023

Clustering Metrics Clustering is an unsupervised learning technique where data points are grouped into clusters based on their similarities or proximity. Evaluation metrics include: Silhouette Coefficient - Measures the compactness and separation of clusters.

ML 52

ML ML Clustering Cross Validation 52

AWS Machine Learning Blog

FEBRUARY 25, 2025

SVM-based classifier: Amazon Titan Embeddings In this scenario, it is likely that user interactions belonging to the three main categories ( Conversation , Services , and Document_Translation ) form distinct clusters or groups within the embedding space. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

Algorithm 70

Algorithm Machine Learning Machine Learning K-nearest Neighbors 70

Pickl AI

JULY 12, 2024

Public Datasets: Utilising publicly available datasets from repositories like Kaggle or government databases. Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. K-Means Clustering K-means partition data points into K clusters based on similarities in feature space.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). Understanding the differences between SQL and NoSQL databases is crucial for students.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

NOVEMBER 28, 2024

Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. databases, CSV files). Validation strategies, such as cross-validation, help assess a model’s generalisation ability and prevent overfitting.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

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. This data can come from databases, APIs, or public datasets. Once you have your data, preprocessing is the next step.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52