Udemy Complete Machine Learning and Deep Learning With H2O in R

Udemy Complete Machine Learning and Deep Learning With H2O in R
     Get Bonus Downloads Here.url -


     001 Brief Introduction.mp4 -


     001 Brief Introduction_en.srt -


     002 Data and Code.html -


     003 Install R and RStudio.mp4 -


     003 Install R and RStudio_en.srt -


     004 Common data types.mp4 -


     004 Common data types_en.srt -


     005 Install H2o.mp4 -


     005 Install H2o_en.srt -


     _L10_h2o_externalData.txt -


     _L6_csv-excel.txt -


     _L7_readHTML_xml.txt -


     _L8_readHTML_rcurl.txt -


     _L9_readJson.txt -


     _Resp1.csv -


     _boston1.xls -


     _glassClass.csv -


     _skorea.json -


     _winequality-red.csv -


     _L11_removeNA.txt -


     _L12_pipeop.txt -


     _L13_tidyv1.txt -


     _L14_EDA.txt -


     _L18_kmeans.txt -


     _L20_pca.txt -


     _Seabmass_typ.csv -


     _covtype.csv -


     _L22_glm_binary.txt -


     _L24_rf_binary.txt -


     _L26_rf_multi.txt -


     _L27_gbm_binary.txt -


     _LoanDefault.csv -


     _covtype.csv -


     _L31_h2o_ann.txt -


     _L32_h2o-dnn-3hidden.txt -


     _L33_h2o-dnn-2hidden.txt -


     _L34_h2o_varimp.txt -


     _L35_h2o_regression.txt -


     _dataset.csv -


     _L38_h2o_ann_unsup.txt -


     _L39_h2o_autoencoders.txt -


     _cancer_tumor.csv -


     _creditcard.csv -


     L10_h2o_externalData.txt -


     L6_csv-excel.txt -


     L7_readHTML_xml.txt -


     L8_readHTML_rcurl.txt -


     L9_readJson.txt -


     Resp1.csv -


     boston1.xls -


     glassClass.csv -


     skorea.json -


     winequality-red.csv -


     L11_removeNA.txt -


     L12_pipeop.txt -


     L13_tidyv1.txt -


     L14_EDA.txt -


     L18_kmeans.txt -


     L20_pca.txt -


     Seabmass_typ.csv -


     covtype.csv -


     L22_glm_binary.txt -


     L24_rf_binary.txt -


     L26_rf_multi.txt -


     L27_gbm_binary.txt -


     LoanDefault.csv -


     covtype.csv -


     L31_h2o_ann.txt -


     L32_h2o-dnn-3hidden.txt -


     L33_h2o-dnn-2hidden.txt -


     L34_h2o_varimp.txt -


     L35_h2o_regression.txt -


     dataset.csv -


     L38_h2o_ann_unsup.txt -


     L39_h2o_autoencoders.txt -


     cancer_tumor.csv -


     creditcard.csv -


     001 Read CSV and Excel Data.mp4 -


     001 Read CSV and Excel Data_en.srt -


     002 Read in Data from Online HTML Tables-Part 1.mp4 -


     002 Read in Data from Online HTML Tables-Part 1_en.srt -


     003 Read in Data from Online HTML Tables-Part 2.mp4 -


     003 Read in Data from Online HTML Tables-Part 2_en.srt -


     004 Read External Data into H2o.mp4 -


     004 Read External Data into H2o_en.srt -


     001 Basic Data Cleaning in R_ Remove NA.mp4 -


     001 Basic Data Cleaning in R_ Remove NA_en.srt -


     002 Pre-processing Tasks and the Pipe Operator.mp4 -


     002 Pre-processing Tasks and the Pipe Operator_en.srt -


     003 Introduction to Pipe Operators.mp4 -


     003 Introduction to Pipe Operators_en.srt -


     004 The Tidyverse Package.mp4 -


     004 The Tidyverse Package_en.srt -


     005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R.mp4 -


     005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R_en.srt -


     001 What is Machine Learning_.mp4 -


     001 What is Machine Learning__en.srt -


     002 Difference Between Supervised & Unsupervised Learning.mp4 -


     002 Difference Between Supervised & Unsupervised Learning_en.srt -


     001 Theory of k-Means Clustering.mp4 -


     001 Theory of k-Means Clustering_en.srt -


     002 Implement k-Means Classification.mp4 -


     002 Implement k-Means Classification_en.srt -


     003 Principal Component Analysis (PCA)_ Theory.mp4 -


     003 Principal Component Analysis (PCA)_ Theory_en.srt -


     004 Implement PCA With H2O.mp4 -


     004 Implement PCA With H2O_en.srt -


     001 Generalized Linear Models (GLMs)_ Theory.mp4 -


     001 Generalized Linear Models (GLMs)_ Theory_en.srt -


     002 GLMs For Binary Classification.mp4 -


     002 GLMs For Binary Classification_en.srt -


     003 Common Algorithms For Supervised Classification.mp4 -


     003 Common Algorithms For Supervised Classification_en.srt -


     004 Implement Random Forest For Binary Classification Problem.mp4 -


     004 Implement Random Forest For Binary Classification Problem_en.srt -


     005 Measures of Accuracy_Binary Classification.mp4 -


     005 Measures of Accuracy_Binary Classification_en.srt -


     006 Implement Random Forest For Multiple Classification Problem.mp4 -


     006 Implement Random Forest For Multiple Classification Problem_en.srt -


     007 Gradient Boosting Machines (GBM) for Binary Classification.mp4 -


     007 Gradient Boosting Machines (GBM) for Binary Classification_en.srt -


     001 A Brief Introduction to Artificial Intelligence.mp4 -


     001 A Brief Introduction to Artificial Intelligence_en.srt -


     002 Theory Behind ANN and DNN.mp4 -


     002 Theory Behind ANN and DNN_en.srt -


     003 Implement an ANN with H2o For Multi-Class Supervised Classification.mp4 -


     003 Implement an ANN with H2o For Multi-Class Supervised Classification_en.srt -


     004 What Are Activation Functions_ Theory.mp4 -


     004 What Are Activation Functions_ Theory_en.srt -


     005 Implement a DNN with H2o For Multi-Class Supervised Classification.mp4 -


     005 Implement a DNN with H2o For Multi-Class Supervised Classification_en.srt -


     006 Implement a (Less Intensive) DNN with H2o For Supervised Classification.mp4 -


     006 Implement a (Less Intensive) DNN with H2o For Supervised Classification_en.srt -


     007 Identify the Important Predictors.mp4 -


     007 Identify the Important Predictors_en.srt -


     008 DNN For Regression.mp4 -


     008 DNN For Regression_en.srt -


     001 Autoencoders for Unsupervised Learning.mp4 -


     001 Autoencoders for Unsupervised Learning_en.srt -


     002 Unsupervised Classification with H2o.mp4 -


     002 Unsupervised Classification with H2o_en.srt -


     003 More Autoencoders _ Credit Card Fraud Detection.mp4 -


     003 More Autoencoders _ Credit Card Fraud Detection_en.srt -


     004 Use the Autoencoder Model for Anomaly Detection.mp4 -


     004 Use the Autoencoder Model for Anomaly Detection_en.srt -


     Bonus Resources.txt -

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