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[ FreeCourseWeb com ] Udemy - Complete Machine Learning and Deep Learning With H2O in R
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01 - Welcome To The Course
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
__MACOSX
data_code_H2O-R
section2
_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
section3
_L11_removeNA.txt
_L12_pipeop.txt
_L13_tidyv1.txt
_L14_EDA.txt
section5
_L18_kmeans.txt
_L20_pca.txt
_Seabmass_typ.csv
_covtype.csv
section6
_L22_glm_binary.txt
_L24_rf_binary.txt
_L26_rf_multi.txt
_L27_gbm_binary.txt
_LoanDefault.csv
_covtype.csv
section7
_L31_h2o_ann.txt
_L32_h2o-dnn-3hidden.txt
_L33_h2o-dnn-2hidden.txt
_L34_h2o_varimp.txt
_L35_h2o_regression.txt
_dataset.csv
section8
_L38_h2o_ann_unsup.txt
_L39_h2o_autoencoders.txt
_cancer_tumor.csv
_creditcard.csv
data_code_H2O-R
section2
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
section3
L11_removeNA.txt
L12_pipeop.txt
L13_tidyv1.txt
L14_EDA.txt
section5
L18_kmeans.txt
L20_pca.txt
Seabmass_typ.csv
covtype.csv
section6
L22_glm_binary.txt
L24_rf_binary.txt
L26_rf_multi.txt
L27_gbm_binary.txt
LoanDefault.csv
covtype.csv
section7
L31_h2o_ann.txt
L32_h2o-dnn-3hidden.txt
L33_h2o-dnn-2hidden.txt
L34_h2o_varimp.txt
L35_h2o_regression.txt
dataset.csv
section8
L38_h2o_ann_unsup.txt
L39_h2o_autoencoders.txt
cancer_tumor.csv
creditcard.csv
02 - Read in Data From Different Sources
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
03 - Data Preprocessing (Briefly)
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
04 - Some Theoretical Foundations
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
05 - Unsupervised Classification with H2o
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
06 - Supervised Classification with H2O
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
07 - Artificial Neural Networks (ANN) and Deep Neural Networks With H2O
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
08 - Deep Learning Based Unsupervised Classification
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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