Downloaded 78 times
Uploaded bySimon Elliston Ball
Machine learning without the PhD - azure ml
The document discusses machine learning concepts and tools, emphasizing the use of Azure ML by Simon Elliston Ball. It covers various learning types, data cleaning, and model evaluation techniques, while highlighting that 80% of a data scientist's time is spent on data preparation. The content also mentions the programming language R and its libraries available in Azure ML for model deployment.
More Related Content
Similar to Machine learning without the PhD - azure ml
Machine learning without the PhD - azure ml
- 1. Machine Learning withoutthe PhD - Azure ML Simon Elliston Ball Head of Big Data @sireb http://bit.ly/learningAzureML #learnAzureML
- 2.
- 3. Skynet. Self awareflying robots. …in the cloud
- 5.
- 6. Goals of machinelearning Prediction Explanation Automation Anomaly detection
- 7. Prediction “Prediction isvery difficult, especially when it’s about the future.” - Niels Bohr
- 8. Classification Binary Aor B A or !A Multi-Class A, B, C, D, E Binary Multi-Class A or !A, ok: B or !B, how about C? …
- 9. Types of learning Regression Clustering Classification Natural Language Processing Deep learning
- 10. Science, with Data Hypothesis Model Test Evaluate Conclude
- 11. Cleaning 80% ofany data scientists time is spent cleaning the data leaving just 20% to complain about cleaning the data https://www.flickr.com/photos/derekgavey/4283300990
- 12. Cleaning Missing values Normalization Scaling https://www.flickr.com/photos/derekgavey/4283300990
- 13. Cleaning Missing values Normalization Scaling Filtering https://www.flickr.com/photos/derekgavey/4283300990 Signal Processing Complex Models Simple Thresholds Smoothing Moving Average
- 14. Cleaning Missing values Normalization Scaling Filtering Meta data and naming https://www.flickr.com/photos/derekgavey/4283300990 Column Names Projection Type Cleanups
- 15. Splits Training Testing Validation https://www.flickr.com/photos/tabor-roeder/11606138806 80% 20% e.g. when Comparing different models
- 16. Modeling Continuous? Discrete? Labeled? Supervised Un-Labeled? Unsupervised
- 17.
- 18. Decision Trees Weather!= Snow Weather = Snow Temp > 20C Wind > 20
- 19.
- 20.
- 21.
- 22. Scoring Apply modelto your data Outputs: Result The probability that the result is sort of right
- 23. Demo time… AzurePortal -> Machine Learning Studio New Experiment https://www.flickr.com/photos/mgdtgd/144569790
- 24. Evaluating Is myclassifier any good? False Negative True Positive False Positive True Negative Precision: TP/(TP+FP) Accuracy (TP+TN)/(P+N)
- 25. Evaluating How farout was I? Error distance functions: Mean squared error Mean absolute error R2 Coefficient of Determination https://www.flickr.com/photos/dahlstroms/3945656390
- 26.
- 27. R you ready? Programming Language based on S+ By Statisticians For statisticians Many many libraries Included in Azure ML…
- 28. R you ready? abc abind actuar ade4 AdMit aod ape approximator arm arules arulesViz ash assertthat AtelieR BaBooN BACCO BaM bark BAS base BayesDA bayesGARCH bayesm bayesmix bayesQR bayesSurv Bayesthresh BayesTree BayesValidate BayesX BayHaz bbemkr BCBCSF BCE bclust bcp BenfordTests bfp BH bisoreg bit bitops BLR BMA Bmix BMS bnlearn boa Bolstad boot bootstrap bqtl BradleyTerry2 brew brglm bspec bspmma BVS cairoDevice calibrator car caret catnet caTools chron class cluster clusterSim coda codetools coin colorspace combinat compiler corpcor cslogistic ctv cubature data.table datasets date dclone deal Deducer DeducerExtras deldir DEoptimR deSolve devtools dichromat digest distrom dlm doSNOW dplyr DPpackage dse e1071 EbayesThresh ebdbNet effects emulator ensembleBMA entropy EvalEst evaluate evdbayes evora exactLoglinTest expm extremevalues factorQR faoutlier fitdistrplus FME foreach forecast foreign formatR Formula fracdiff gam gamlr gbm gclus gdata gee genetics geoR geoRglm geosphere ggmcmc ggplot2 glmmBUGS glmnet gmodels gmp gnm googlePublicDa ta googleVis GPArotation gplots graphics grDevices gregmisc grid gridExtra growcurves grpreg gsubfn gtable gtools gWidgets gWidgetsRGtk2 haplo.stats hbsae hdrcde heavy hflights HH HI highr Hmisc htmltools httpuv httr IBrokers igraph intervals iplots ipred irr iterators JavaGD JGR kernlab KernSmooth KFKSDS kinship2 kknn klaR knitr ks labeling Lahman lars lattice latticeExtra lava lavaan leaps LearnBayes limSolve lme4 lmm lmPerm lmtest locfit lpSolve magic magrittr mapdata mapproj maps maptools maptree markdown MASS MasterBayes Matrix matrixcalc MatrixModels maxent maxLik mcmc MCMCglmm MCMCpack memoise methods mgcv mice microbenchmar k mime minpack.lm minqa misc3d miscF miscTools mixtools mlbench mlogitBMA mnormt MNP modeltools mombf monomvn monreg mosaic MSBVAR msm multcomp multicool munsell mvoutlier mvtnorm ncvreg nlme NLP nnet numbers numDeriv openNLP openNLPdata OutlierDC OutlierDM outliers pacbpred parallel partitions party PAWL pbivnorm pcaPP permute pls plyr png polynom PottsUtils predmixcor PresenceAbse nce prodlim profdpm profileModel proto pscl psych quadprog quantreg qvcalc R.matlab R.methodsS3 R.oo R.utils R2HTML R2jags R2OpenBUGS R2WinBUGS ramps RandomFields randomForest RArcInfo raster rbugs RColorBrewer Rcpp RcppArmadillo rcppbugs RcppEigen RcppExamples RCurl relimp reshape reshape2 rgdal rgeos rgl RGraphics RGtk2 RJaCGH rjags rJava RJSONIO robComposition s robustbase RODBC rootSolve roxygen roxygen2 rpart rrcov rscproxy RSGHB RSNNS RTextTools RUnit runjags Runuran rworldmap rworldxtra SampleSizeMe ans SampleSizePro portions sandwich sbgcop scales scapeMCMC scatterplot3d sciplot segmented sem seriation setRNG sgeostat shapefiles shiny SimpleTable slam smoothSurv sna snow SnowballC snowFT sp spacetime SparseM spatial spBayes spdep spikeslab splancs splines spTimer stats stats4 stochvol stringr strucchange stsm stsm.class SuppDists survival svmpath tau tcltk tcltk2 TeachingDemo s tensorA testthat textcat textir tfplot tframe tgp TH.data timeDate tm tools translations tree tseries tsfa tsoutliers TSP UsingR utils varSelectIP vcd vegan VGAM VIF whisker wordcloud XLConnect XML xtable xts yaml zic zipfR zoo
- 29. R you ready? Two Data Sets enter. One Data Set leaves. (And a chart if you’re lucky)
- 30. Production Once morefrom the top… Modeled in R Applied in C#, Java, whatever, but not R
- 31. Publish a webservice. https://www.flickr.com/photos/jurvetson/6858583426
- 32. Thanks! Simon EllistonBall simon@simonellistonball.com @sireb
- 33. Questions? Simon EllistonBall simon@simonellistonball.com @sireb http://bit.ly/learningAzureML #learnAzureML