FreeCourseWeb com Udemy Complete Machine Learning and Deep Learning With H2O in

FreeCourseWeb com Udemy Complete Machine Learning and Deep Learning With H2O in

Name	[ FreeCourseWeb.com ] Udemy - Complete Machine Learning and Deep Learning With H2O in R	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.3 GB
Total Files	147
Last Seen	2025-05-04 23:42
Hash	C420B4EBBA273AA8F8CEB60924A185C607977C00

/
Get Bonus Downloads Here.url	0.2 KB
/.../01 - Welcome To The Course/
001 Brief Introduction.mp4	28.4 MB
001 Brief Introduction_en.srt	3.0 KB
002 Data and Code.html	0.1 KB
003 Install R and RStudio.mp4	67.6 MB
003 Install R and RStudio_en.srt	7.1 KB
004 Common data types.mp4	48.5 MB
004 Common data types_en.srt	4.2 KB
005 Install H2o.mp4	87.2 MB
005 Install H2o_en.srt	5.4 KB
/.../data_code_H2O-R/section2/
_L10_h2o_externalData.txt	0.6 KB
_L6_csv-excel.txt	0.2 KB
_L7_readHTML_xml.txt	0.2 KB
_L8_readHTML_rcurl.txt	0.2 KB
_L9_readJson.txt	0.6 KB
_Resp1.csv	0.2 KB
_boston1.xls	0.2 KB
_glassClass.csv	0.6 KB
_skorea.json	0.6 KB
_winequality-red.csv	0.2 KB
/.../data_code_H2O-R/section3/
_L11_removeNA.txt	0.3 KB
_L12_pipeop.txt	0.7 KB
_L13_tidyv1.txt	0.6 KB
_L14_EDA.txt	0.2 KB
/.../data_code_H2O-R/section5/
_L18_kmeans.txt	0.3 KB
_L20_pca.txt	0.5 KB
_Seabmass_typ.csv	0.3 KB
_covtype.csv	0.2 KB
/.../data_code_H2O-R/section6/
_L22_glm_binary.txt	0.3 KB
_L24_rf_binary.txt	0.5 KB
_L26_rf_multi.txt	0.3 KB
_L27_gbm_binary.txt	0.5 KB
_LoanDefault.csv	0.2 KB
_covtype.csv	0.2 KB
/.../data_code_H2O-R/section7/
_L31_h2o_ann.txt	0.6 KB
_L32_h2o-dnn-3hidden.txt	0.6 KB
_L33_h2o-dnn-2hidden.txt	0.6 KB
_L34_h2o_varimp.txt	0.6 KB
_L35_h2o_regression.txt	0.6 KB
_dataset.csv	0.6 KB
/.../data_code_H2O-R/section8/
_L38_h2o_ann_unsup.txt	0.6 KB
_L39_h2o_autoencoders.txt	0.6 KB
_cancer_tumor.csv	0.6 KB
_creditcard.csv	0.6 KB
/.../data_code_H2O-R/section2/
L10_h2o_externalData.txt	0.6 KB
L6_csv-excel.txt	0.7 KB
L7_readHTML_xml.txt	0.5 KB
L8_readHTML_rcurl.txt	0.8 KB
L9_readJson.txt	1.3 KB
Resp1.csv	0.3 KB
boston1.xls	59.4 KB
glassClass.csv	10.1 KB
skorea.json	3.7 KB
winequality-red.csv	84.2 KB
/.../data_code_H2O-R/section3/
L11_removeNA.txt	1.5 KB
L12_pipeop.txt	0.9 KB
L13_tidyv1.txt	0.4 KB
L14_EDA.txt	1.1 KB
/.../data_code_H2O-R/section5/
L18_kmeans.txt	0.7 KB
L20_pca.txt	1.8 KB
Seabmass_typ.csv	29.9 KB
covtype.csv	75.2 MB
/.../data_code_H2O-R/section6/
L22_glm_binary.txt	1.8 KB
L24_rf_binary.txt	1.4 KB
L26_rf_multi.txt	2.6 KB
L27_gbm_binary.txt	1.4 KB
LoanDefault.csv	458.7 KB
covtype.csv	75.2 MB
/.../data_code_H2O-R/section7/
L31_h2o_ann.txt	1.2 KB
L32_h2o-dnn-3hidden.txt	2.8 KB
L33_h2o-dnn-2hidden.txt	1.3 KB
L34_h2o_varimp.txt	1.3 KB
L35_h2o_regression.txt	1.0 KB
dataset.csv	133.0 MB
/.../data_code_H2O-R/section8/
L38_h2o_ann_unsup.txt	1.1 KB
L39_h2o_autoencoders.txt	1.1 KB
cancer_tumor.csv	125.2 KB
creditcard.csv	150.8 MB
/.../02 - Read in Data From Different Sources/
001 Read CSV and Excel Data.mp4	116.7 MB
001 Read CSV and Excel Data_en.srt	11.6 KB
002 Read in Data from Online HTML Tables-Part 1.mp4	19.0 MB
002 Read in Data from Online HTML Tables-Part 1_en.srt	4.6 KB
003 Read in Data from Online HTML Tables-Part 2.mp4	87.5 MB
003 Read in Data from Online HTML Tables-Part 2_en.srt	7.7 KB
004 Read External Data into H2o.mp4	63.8 MB
004 Read External Data into H2o_en.srt	5.9 KB
/.../03 - Data Preprocessing (Briefly)/
001 Basic Data Cleaning in R_ Remove NA.mp4	141.1 MB
001 Basic Data Cleaning in R_ Remove NA_en.srt	17.7 KB
002 Pre-processing Tasks and the Pipe Operator.mp4	96.4 MB
002 Pre-processing Tasks and the Pipe Operator_en.srt	9.2 KB
003 Introduction to Pipe Operators.mp4	96.4 MB
003 Introduction to Pipe Operators_en.srt	9.2 KB
004 The Tidyverse Package.mp4	32.9 MB
004 The Tidyverse Package_en.srt	3.9 KB
005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R.mp4	119.8 MB
005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R_en.srt	6.8 KB
/.../04 - Some Theoretical Foundations/
001 What is Machine Learning_.mp4	73.0 MB
001 What is Machine Learning__en.srt	7.4 KB
002 Difference Between Supervised & Unsupervised Learning.mp4	73.0 MB
002 Difference Between Supervised & Unsupervised Learning_en.srt	7.4 KB
/.../05 - Unsupervised Classification with H2o/
001 Theory of k-Means Clustering.mp4	19.1 MB
001 Theory of k-Means Clustering_en.srt	2.1 KB
002 Implement k-Means Classification.mp4	49.7 MB
002 Implement k-Means Classification_en.srt	5.3 KB
003 Principal Component Analysis (PCA)_ Theory.mp4	25.6 MB
003 Principal Component Analysis (PCA)_ Theory_en.srt	3.4 KB
004 Implement PCA With H2O.mp4	159.8 MB
004 Implement PCA With H2O_en.srt	16.3 KB
/.../06 - Supervised Classification with H2O/
001 Generalized Linear Models (GLMs)_ Theory.mp4	40.8 MB
001 Generalized Linear Models (GLMs)_ Theory_en.srt	6.0 KB
002 GLMs For Binary Classification.mp4	87.0 MB
002 GLMs For Binary Classification_en.srt	10.3 KB
003 Common Algorithms For Supervised Classification.mp4	25.1 MB
003 Common Algorithms For Supervised Classification_en.srt	13.0 KB
004 Implement Random Forest For Binary Classification Problem.mp4	124.6 MB
004 Implement Random Forest For Binary Classification Problem_en.srt	11.8 KB
005 Measures of Accuracy_Binary Classification.mp4	60.9 MB
005 Measures of Accuracy_Binary Classification_en.srt	5.5 KB
006 Implement Random Forest For Multiple Classification Problem.mp4	90.5 MB
006 Implement Random Forest For Multiple Classification Problem_en.srt	10.2 KB
007 Gradient Boosting Machines (GBM) for Binary Classification.mp4	69.7 MB
007 Gradient Boosting Machines (GBM) for Binary Classification_en.srt	6.8 KB
/.../07 - Artificial Neural Networks (ANN) and Deep Neural Networks With H2O/
001 A Brief Introduction to Artificial Intelligence.mp4	100.2 MB
001 A Brief Introduction to Artificial Intelligence_en.srt	10.5 KB
002 Theory Behind ANN and DNN.mp4	98.2 MB
002 Theory Behind ANN and DNN_en.srt	11.6 KB
003 Implement an ANN with H2o For Multi-Class Supervised Classification.mp4	114.5 MB
003 Implement an ANN with H2o For Multi-Class Supervised Classification_en.srt	11.2 KB
004 What Are Activation Functions_ Theory.mp4	91.0 MB
004 What Are Activation Functions_ Theory_en.srt	7.4 KB
005 Implement a DNN with H2o For Multi-Class Supervised Classification.mp4	64.3 MB
005 Implement a DNN with H2o For Multi-Class Supervised Classification_en.srt	7.4 KB
006 Implement a (Less Intensive) DNN with H2o For Supervised Classification.mp4	32.2 MB
006 Implement a (Less Intensive) DNN with H2o For Supervised Classification_en.srt	4.5 KB
007 Identify the Important Predictors.mp4	100.4 MB
007 Identify the Important Predictors_en.srt	8.5 KB
008 DNN For Regression.mp4	60.2 MB
008 DNN For Regression_en.srt	4.5 KB
/.../08 - Deep Learning Based Unsupervised Classification/
001 Autoencoders for Unsupervised Learning.mp4	27.0 MB
001 Autoencoders for Unsupervised Learning_en.srt	2.3 KB
002 Unsupervised Classification with H2o.mp4	112.3 MB
002 Unsupervised Classification with H2o_en.srt	5.8 KB
003 More Autoencoders _ Credit Card Fraud Detection.mp4	58.2 MB
003 More Autoencoders _ Credit Card Fraud Detection_en.srt	4.2 KB
004 Use the Autoencoder Model for Anomaly Detection.mp4	71.4 MB
004 Use the Autoencoder Model for Anomaly Detection_en.srt	6.1 KB
/~Get Your Files Here !/
Bonus Resources.txt	0.4 KB
Total files 147