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Top KDnuggets tweets, Mar 21-23: Machine Learning in Parallel with SVM; Good Data Sets for Data Science Practice


Machine Learning in Parallel with SVM, GLM; Good Data Sets for Data Science Practice: Big enough, requires data engineering, rich; Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics; Top 45 #BigData Tools and Platforms for Developers



Most popular @KDnuggets tweets for Mar 21-23 were


Most viewed:
Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting buff.ly/1jiSsoB

Most Retweeted:
Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics buff.ly/1jiIByT Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics

Most Favorited:
Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting buff.ly/1jiSsoB

Top 10 Tweets
  1. Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting buff.ly/1jiSsoB
  2. Good Data Sets for Data Science Practice: Big enough, requires data engineering, rich - MovieLens, Airlines data buff.ly/1jqFUal
  3. Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics buff.ly/1jiIByT
  4. Top 45 #BigData Tools and Platforms for Developers, from @splicemachine to @hpccsystems buff.ly/1leROZ9
  5. Data Just Right: a Google engineer and #BigData hacker, writes for professionals who need practical solutions buff.ly/1h86XVb
  6. The problem with data journalism - it is not science, be aware that even the best charts can be biased buff.ly/1jiITWy
  7. Data Mining Book Review by Sandro Saitta: "Naked Statistics" very readable, often funny, a delicious surprise buff.ly/1gjSF8M
  8. Test your "numbersense" - what do you notice in this distribution? buff.ly/1h5JBQ4
  9. How to get right data scientists to ask the wrong questions - fierce curiosity is what marks great data scientists buff.ly/1dlN7dK
  10. Facebook DeepFace #AI software matches faces as well as humans (97.25% vs 97.53% accuracy) buff.ly/1in6kdR