Data Science Dojo

JUNE 7, 2023

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science 195

Data Science Data Visualization Data Scientist Machine Learning 195

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? By leveraging anomaly detection, we can uncover hidden irregularities in transaction data that may indicate fraudulent behavior.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

PyImageSearch

OCTOBER 21, 2024

In the first part of our Anomaly Detection 101 series, we learned the fundamentals of Anomaly Detection and saw how spectral clustering can be used for credit card fraud detection. This method helps in identifying fraudulent transactions by grouping similar data points and detecting outliers. detection of potential failures or issues).

Algorithm 107

Algorithm Deep Learning Deep Learning Data Preparation 107

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

AWS 93

AWS ML ML Clustering 93

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. Representation models encode meaningful features from raw data for use in classification, clustering, or information retrieval tasks. Book a demo today.

Data Science 64

Data Science AI AI Machine Learning 64

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. Representation models encode meaningful features from raw data for use in classification, clustering, or information retrieval tasks. Book a demo today.

Data Science 52

Data Science Data Scientist AI AI 52

AWS Machine Learning Blog

AUGUST 15, 2024

You need data engineering expertise and time to develop the proper scripts and pipelines to wrangle, clean, and transform data. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets. These features can find temporal patterns in the data that can influence the baseFare.

ML 119

ML ML Data Preparation AWS 119