Data Science #34
Andriy Burkov
PhD in AI, ML at TalentNeuron, author of ?? The Hundred-Page Machine Learning Book and ?? the Machine Learning Engineering book
In this issue: time series forecasting with machine learning models; logarithms and heteroskedasticity; how to stay sane when programming distributed systems; cleaning medical data in R; why is dataset search still so hard?; and more.
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??Founder of AIBoost Marketing, Digital Marketing Strategist | Elevating Brands with Data-Driven SEO and Engaging Content??
2 周Exciting newsletter! ?? Dive into time series forecasting, stay sane with distributed systems, and conquer dataset search struggles. Subscribe for more insights! #DataScience #MachineLearning #StaySane
CEO at xLM | Transforming Life Sciences with AI & ML | Pioneer in GxP Continuous Validation |
2 周Absolutely fascinating topics covered in this edition! One thing often overlooked in time series forecasting is the potential of hybrid models that combine classical statistical methods with modern machine learning techniques. This fusion can often yield superior predictive performance. Additionally, the challenge of dataset search could be mitigated by leveraging advanced natural language processing algorithms to enhance data discoverability. Excited to see how these discussions evolve!
Founder & Fractional Chief AI Officer building AI First Engineering Products & Organisations | Passionate about the intersection of Art, Design & Technology | Fine Art Photographer
2 周Exactly Andriy, Dataset search can be so tricky.