By Sirisha, contributing writer for Analyticsinsight.net
Data is core to any ML or AI project and it is estimated that roughly the project needs ten times the examples your project has degrees of freedom. Having heaps of machine learning datasets is very crucial that sometimes even after you think you have enough data you might end up concluding the existing data is not enough. Having data at that scale though might result in overfitting, at times, it is absolutely necessary for the algorithms to learn about all details and noise. Machine learning algorithms are in fact designed to improvise with time and for this, they need quality data from time to time. However, machine learning experts find it difficult to source data continuously to keep the algorithm working. Analytics Insight lists out the top 10 sources for finding machine learning datasets in 2022.
By Robin Kearey, contributing writer for Hackaday.com
Golf can be a frustrating game to learn: it takes countless hours of practice to get anywhere near the perfect swing. While some might be lucky enough to have a pro handy every time they’re on the driving range or putting green, most of us will have to get by with watching the ball’s motion and using that to figure out what we’re doing wrong.
By A contributing writer for Qualitymag.com
Manufacturers can boost their bottom lines by leveraging predictive analytics. By automating internal and external data to cultivate insight into customer demand, they can avoid machine downtime and save money.
By Erik Larson, contributing writer for Forbes.com
Decision intelligence has been a tech buzzword for several years. Still, it wasn’t until last October that technology analyst firm Gartner named it a 2022 Top Trend and put a clear definition in place. By doing so, Gartner changed decision intelligence from a vague marketing term to an increasingly important business strategy.
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