Flipboard

JANUARY 24, 2025

The proposed Q-BGWO-SQSVM approach utilizes an improved quantum-inspired binary Grey Wolf Optimizer and combines it with SqueezeNet and Support Vector Machines to exhibit sophisticated performance. SqueezeNet’s fire modules and complex bypass mechanisms extract distinct features from mammography images.

Support Vector Machines 117

Support Vector Machines Cross Validation Database Machine Learning 117

AWS Machine Learning Blog

FEBRUARY 25, 2025

Classification algorithms like support vector machines (SVMs) are especially well-suited to use this implicit geometry of the data. To determine the best parameter values, we conducted a grid search with 10-fold cross-validation, using the F1 multi-class score as the evaluation metric.

Algorithm 69

Algorithm Machine Learning Machine Learning K-nearest Neighbors 69

Pickl AI

JULY 12, 2024

Public Datasets: Utilising publicly available datasets from repositories like Kaggle or government databases. Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. Web Scraping : Extracting data from websites and online sources.

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

Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. databases, CSV files).

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

Structured data refers to neatly organised data that fits into tables, such as spreadsheets or databases, where each column represents a feature and each row represents an instance. This data can come from databases, APIs, or public datasets. Without high-quality data, even the most sophisticated model will fail.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52