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Residual Analysishttps://github.com/AndyMeng/Machine-Learning-Tutorials#residuals-
Interpreting plot.lm() in Rhttp://stats.stackexchange.com/questions/58141/interpreting-plot-lm
How to interpret a QQ plot?http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1
Interpreting Residuals vs Fitted Plothttp://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions
Outliershttps://github.com/AndyMeng/Machine-Learning-Tutorials#outliers-
How should outliers be dealt with?http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis
Elastic Nethttps://en.wikipedia.org/wiki/Elastic_net_regularization
Regularization and Variable Selection via the Elastic Nethttps://web.stanford.edu/~hastie/Papers/elasticnet.pdf
Resampling Explainedhttps://en.wikipedia.org/wiki/Resampling_(statistics)
Partioning data set in Rhttp://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations
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2http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-testing-and.html
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Cross Validation vs Bootstrap to estimate prediction errorhttp://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio
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Part 3https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/
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What would Shakespeare say (NLP Tutorial)https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/
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xgboost surveyhttps://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey
Practical XGBoost in Python online course (free)http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python
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Python Codehttps://gist.github.com/tristanwietsma/5486024
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adaBag R packagehttps://cran.r-project.org/web/packages/adabag/adabag.pdf
Tutorialhttp://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf
ujjwalkarn.github.io/Machine-Learning-Tutorialshttp://ujjwalkarn.github.io/Machine-Learning-Tutorials
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