Revision as of 06:57, 8 September 2020

Building a Business Case for your Machine Learning Idea This presentation will discuss building a business model for your machine learning idea. Our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model. Introducing the guidance questions, Who is your customer?, What is your business problem?, What are your data sources?, Do you have the right dataset?, What are key ethical/legal/compliance considerations?, Have you named & described your solution?, Do your customers understand your pricing model?, Who & how will you sell your solution?, How will you Maintain/Support/Service your solution?, Will your solution help generate revenue or save Costs/Time?, Resources

Common Mistakes]

Common mistakes made in Machine Learning Models Analytics University You will learn the common mistake people make while building machine learning models. Machine learning models are easy to build but need attention to details. The common mistakes could be: 1- taking Default Loss Function for granted, 2- Using one Algorithm / Method For All Problems: 3- Ignoring Outliers: 4- No Proper Dealing With Cyclical Features, 5- L1/L2 Regularisation Without Standardization, 6- Interpreting Coefficients From Linear or Logistic Regressions as features importance. Analytics Study Pack : http://analyticuniversity.com/

Cameron Davidson Pilon: Mistakes I've Made PyData Seattle 2015 In this humbling talk, I'll describe some mistakes I've made in working in statistics and machine learning. I'll describe my original intentions, symptoms, how I eventually discovered the mistake, and possibly even a solution. The topics include mistakes in A/B testing, Kaggle competitions, data collection, and other fields. In this humbling talk, I'll describe some mistakes I've made in working in statistics and machine learning. I'll describe my original intentions, symptoms, how I eventually discovered the mistake, and possibly even a solution. The topics include mistakes in A/B testing, Kaggle competitions, data collection, and other fields. I'll also introduce some interesting statistical and machine learning counterexamples: examples where our original intuition fails, and solutions to these examples.

AI Simplified: Top 3 Rookie Mistakes in Machine Learning John Boersma, Director of Education at DataRobot, shares his list of the top three rookie mistakes in machine learning for our AI Simplified series. Learn more about simplified AI terms on our wiki page: http://www.datarobot.com/wiki

Top 10 Machine Learning Pitfalls – Mark Landry Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, Mark Landry, one of the world’s leading Kagglers, will review the top 10 common pitfalls and steps to avoid them. View more talks from H2O Open Tour Dallas http://open.h2o.ai/dallas.html Powered by the open source machine learning software H2O.ai. Contributors welcome at http://github.com/h2oai To access slides on H2O open source machine learning software, go to: http://www.slideshare.net/0xdata

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 * [http://www.computerworld.co.nz/article/521906/12_predictive_analytics_screw-ups/ 12 predictive analytics screw-ups | Robert L. Mitchell]
 * [http://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3 Ten quick tips for machine learning in computational biology | Davide Chicco]