Venkata Reddy Konasani

data analysis & predictive modeling course data analysis machine learning data scientist predictive modeling bigdata deep learning r hyper parameters neural networks sas functions sas programs sas business analytics data mining tableau python clasification datasets hadoop k-means clustering arima forecasting trends & forecasting analytics data artificial intelligence bias variance tradeoff variance bias under fitting over fitting auc roc f1 score specificity sensitivity bootstrap cross validation 10-fold cross validation k-fold cross validation cross validation model selection tensor board regularization learning rate statinfer r code options r code boosting algorithm boosting gradient boosting gbm ann code ai gradient descent back propagation information gain decision tree entropy r packages r functions r data model building logistic regression model validation vintage analysis variable selection waterfall analysis credit risk risk analytics step by step learning p-value t-test case study testing of hypothesis analysis time series tableau options presenting data graphs dash boards data visualizations sql ruby qlikview database data visualization kernal svm learning fa pca bigdata sources data sources need of bigdata r basics clutsre analysis data validation data sanitization data exploration stationarity ar process ma process goodness of fit big data baby hadoop meetup understanding data benchmark analysis overall summary & summary by various segments data cleaning & audit objective & scope kpis background control charts multivariate analysis & segmentation tracking basic metrics driver analysis