2010 Data Analysis Machine Learning 
Women in Big Data
APRIL 8, 2025
The session, Business Analytics in Action: Driving Decisions with Data, provided participants with a comprehensive understanding of how analytics can transform business decision-making processes and drive meaningful results. The workshop began with an exploration of the fundamental concepts of business analytics and its evolution over time.
Towards AI
AUGUST 11, 2023
Tabular data has been around for decades and is one of the most common data types used in data analysis and machine learning. Traditionally, tabular data has been used for simply organizing and reporting information.
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AWS Machine Learning Blog
DECEMBER 4, 2024
The structured dataset includes order information for products spanning from 2010 to 2017. This historical data will allow the function to analyze sales trends, product performance, and other relevant metrics over this seven-year period.
NOVEMBER 22, 2023
Full list of new or updated datasets This dataset joins 33 other new or updated datasets on the Registry of Open Data in four categories: climate and weather, geospatial, life sciences, and machine learning (ML). 94-171) Demonstration Noisy Measurement File from United States Census Bureau What are people doing with open data?
O'Reilly Media
SEPTEMBER 19, 2023
But the grouping and summarizing just wasn’t exciting enough for the data addicts. They’d grown tired of learning what is; now they wanted to know what’s next. Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. Those algorithms packaged with scikit-learn?
Dataconomy
JUNE 15, 2023
Nonetheless, starting from around 2010, there has been a renewed surge of interest in the field. This can be attributed primarily to remarkable advancements in computer processing power and the availability of vast amounts of data. Deep learning emerged as a highly promising machine learning technology for various applications.
AWS Machine Learning Blog
OCTOBER 10, 2023
Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Her current areas of interest include federated learning, distributed training, and generative AI.
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