KDnuggets™ News 16:n19, May 25: Explain Machine Learning to Software Engineer; 5 Can’t Miss Machine Learning Projects
How to Explain Machine Learning to a Software Engineer; 5 Machine Learning Projects You Can No Longer Overlook; Doing Data Science: A Kaggle Walkthrough Part 1 - Introduction; The Amazing Power of Word Vectors
Features | Tutorials | Opinions | News | Webcasts | Meetings | Jobs | Quote
Features
How to Explain Machine Learning to a Software Engineer
- 5 Machine Learning Projects You Can No Longer Overlook
- Doing Data Science: A Kaggle Walkthrough Part 1 - Introduction
- The Amazing Power of Word Vectors
- 10 Must Have Data Science Skills, Updated
- Six PAW Chicago Sessions That Show Analytics' Long Reach
How to Explain Machine Learning to a Software EngineerTutorials, Overviews, How-Tos
Opinions
- Tips for Data Scientists: Think Like a Business Executive
- Let Me Hear Your Voice and I'll Tell You How You Feel
- Harnessing Open Data Science for Predictive Analytics (Whitepaper)
- Don't Just Assume That Data Are Interval Scale
- The Data Science Market: 2016 Compensation Insights
News
- CRN Top Business Analytics Vendors 2016
- Top Stories, May 16-22: Annual KDnuggets Analytics Software Poll; How to Explain Machine Learning to Software Engineers
- Top KDnuggets tweets, May 11-17: Vote: What software you used for Analytics, Data Mining, Data Science projects?
Webcasts and Webinars
Meetings
Jobs
Quote
"The errors which arise from the absence of facts are far more numerous and more durable than those which result from unsound reasoning respecting true data." Charles Babbage.
