Category: Data Science

Author: William Vorhies Summary:  Can all AI strategies be defined by a few common needs or are the different AI strategy models sufficiently unique that […] Read More

Author: /u/vogt4nick r/datascience has grown considerably in the past year, from about 70k subscribers in Jan 2019 to 183k subscribers today in Jan 2020. As […] Read More

Author: Max Frolov How to build a project from scratch by using a latest, state-of-the-art AI technology stack? After spending a few years working on […] Read More

Author: Asel Mendis While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with […] Read More

Author: Stephanie Glen Fifty years, ago, the lines between “data analysis” and “statistical analysis” were pretty clear. But as data analysis evolved, those lines became […] Read More

Author: Matthew Dearing We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a […] Read More

Author: Igor Bobriakov Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, […] Read More

Author: Matt Mayo Editor Also: Global #AI Index ranks 54 countries: US is currently ahead. China is second, but growing faster; RStudio Projects and Working […] Read More

Author: mtdearing With the explosion of the field of AI/ML impacting so many applications and industries, there is great value coming out of recent progress. […] Read More

Author: William Vorhies Summary:  Workforce forecasting and scheduling applications are rapidly upgrading their use of AI.  Techniques of time series forecasting ranging from the simple […] Read More