Knowledge Hut

JANUARY 25, 2024

In the context of data science , clean data is crucial because the quality of your data directly impacts the reliability of your analysis and the outcomes of your algorithms. Data cleaning is like ensuring that the ingredients in a recipe are fresh and accurate; otherwise, the final dish won't turn out as expected.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

AltexSoft

MAY 12, 2021

The most advanced AI algorithms achieved the accuracy of almost 97 percent. Otherwise, let’s proceed to the first and most fundamental step in building AI-fueled computer vision tools — data preparation. Computer vision requires plenty of quality data, diverse in gender, race, and geography. Image labeling by experts.

Medical 72

Medical Healthcare Datasets Machine Learning 72

ProjectPro

NOVEMBER 15, 2021

In addition to analytics and data science, RAPIDS focuses on everyday data preparation tasks. This features a familiar DataFrame API that connects with various machine learning algorithms to accelerate end-to-end pipelines without incurring the usual serialization overhead.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

AUGUST 24, 2021

Source Code: Visualize Daily Wikipedia Trends with Hive, Zeppelin, and Airflow (projectpro.io) 7) Data Aggregation Data Aggregation refers to collecting data from multiple sources and drawing insightful conclusions from it. to accumulate data over a given period for better analysis.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52