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Udemy Complete Machine Learning and Deep Learning With H2O in R

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Udemy Complete Machine Learning and Deep Learning With H2O in R

Language:

English

Category:

Other

Size:

3.0 GB

Added:

Oct. 24, 2023, 3:13 p.m.

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Files:

Get Bonus Downloads Here.url 183 bytes
001 Brief Introduction.mp4 27.1 MB
001 Brief Introduction_en.srt 3.0 KB
002 Data and Code.html 70 bytes
003 Install R and RStudio.mp4 64.5 MB
003 Install R and RStudio_en.srt 7.0 KB
004 Common data types.mp4 46.3 MB
004 Common data types_en.srt 4.1 KB
005 Install H2o.mp4 83.1 MB
005 Install H2o_en.srt 5.3 KB
_L10_h2o_externalData.txt 639 bytes
_L6_csv-excel.txt 212 bytes
_L7_readHTML_xml.txt 212 bytes
_L8_readHTML_rcurl.txt 212 bytes
_L9_readJson.txt 584 bytes
_Resp1.csv 212 bytes
_boston1.xls 212 bytes
_glassClass.csv 612 bytes
_skorea.json 584 bytes
_winequality-red.csv 212 bytes
_L11_removeNA.txt 268 bytes
_L12_pipeop.txt 747 bytes
_L13_tidyv1.txt 589 bytes
_L14_EDA.txt 212 bytes
_L18_kmeans.txt 317 bytes
_L20_pca.txt 474 bytes
_Seabmass_typ.csv 266 bytes
_covtype.csv 212 bytes
_L22_glm_binary.txt 317 bytes
_L24_rf_binary.txt 474 bytes
_L26_rf_multi.txt 317 bytes
_L27_gbm_binary.txt 474 bytes
_LoanDefault.csv 176 bytes
_covtype.csv 212 bytes
_L31_h2o_ann.txt 639 bytes
_L32_h2o-dnn-3hidden.txt 639 bytes
_L33_h2o-dnn-2hidden.txt 583 bytes
_L34_h2o_varimp.txt 647 bytes
_L35_h2o_regression.txt 583 bytes
_dataset.csv 612 bytes
_L38_h2o_ann_unsup.txt 639 bytes
_L39_h2o_autoencoders.txt 583 bytes
_cancer_tumor.csv 591 bytes
_creditcard.csv 594 bytes
L10_h2o_externalData.txt 613 bytes
L6_csv-excel.txt 650 bytes
L7_readHTML_xml.txt 506 bytes
L8_readHTML_rcurl.txt 843 bytes
L9_readJson.txt 1.3 KB
Resp1.csv 273 bytes
boston1.xls 58.0 KB
glassClass.csv 9.8 KB
skorea.json 3.6 KB
winequality-red.csv 82.2 KB
L11_removeNA.txt 1.4 KB
L12_pipeop.txt 873 bytes
L13_tidyv1.txt 378 bytes
L14_EDA.txt 1.1 KB
L18_kmeans.txt 707 bytes
L20_pca.txt 1.8 KB
Seabmass_typ.csv 29.2 KB
covtype.csv 71.7 MB
L22_glm_binary.txt 1.7 KB
L24_rf_binary.txt 1.4 KB
L26_rf_multi.txt 2.6 KB
L27_gbm_binary.txt 1.4 KB
LoanDefault.csv 447.9 KB
covtype.csv 71.7 MB
L31_h2o_ann.txt 1.2 KB
L32_h2o-dnn-3hidden.txt 2.7 KB
L33_h2o-dnn-2hidden.txt 1.3 KB
L34_h2o_varimp.txt 1.3 KB
L35_h2o_regression.txt 1017 bytes
dataset.csv 126.9 MB
L38_h2o_ann_unsup.txt 1.0 KB
L39_h2o_autoencoders.txt 1.1 KB
cancer_tumor.csv 122.3 KB
creditcard.csv 143.8 MB
001 Read CSV and Excel Data.mp4 111.3 MB
001 Read CSV and Excel Data_en.srt 11.3 KB
002 Read in Data from Online HTML Tables-Part 1.mp4 18.2 MB
002 Read in Data from Online HTML Tables-Part 1_en.srt 4.5 KB
003 Read in Data from Online HTML Tables-Part 2.mp4 83.5 MB
003 Read in Data from Online HTML Tables-Part 2_en.srt 7.6 KB
004 Read External Data into H2o.mp4 60.8 MB
004 Read External Data into H2o_en.srt 5.8 KB
001 Basic Data Cleaning in R_ Remove NA.mp4 134.5 MB
001 Basic Data Cleaning in R_ Remove NA_en.srt 17.3 KB
002 Pre-processing Tasks and the Pipe Operator.mp4 91.9 MB
002 Pre-processing Tasks and the Pipe Operator_en.srt 9.0 KB
003 Introduction to Pipe Operators.mp4 91.9 MB
003 Introduction to Pipe Operators_en.srt 9.0 KB
004 The Tidyverse Package.mp4 31.4 MB
004 The Tidyverse Package_en.srt 3.8 KB
005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R.mp4 114.3 MB
005 Exploratory Data Analysis(EDA)_ Basic Visualizations with R_en.srt 6.6 KB
001 What is Machine Learning_.mp4 69.7 MB
001 What is Machine Learning__en.srt 7.2 KB
002 Difference Between Supervised & Unsupervised Learning.mp4 69.6 MB
002 Difference Between Supervised & Unsupervised Learning_en.srt 7.2 KB
001 Theory of k-Means Clustering.mp4 18.2 MB
001 Theory of k-Means Clustering_en.srt 2.1 KB
002 Implement k-Means Classification.mp4 47.4 MB
002 Implement k-Means Classification_en.srt 5.2 KB
003 Principal Component Analysis (PCA)_ Theory.mp4 24.4 MB
003 Principal Component Analysis (PCA)_ Theory_en.srt 3.3 KB
004 Implement PCA With H2O.mp4 152.4 MB
004 Implement PCA With H2O_en.srt 15.9 KB
001 Generalized Linear Models (GLMs)_ Theory.mp4 39.0 MB
001 Generalized Linear Models (GLMs)_ Theory_en.srt 5.9 KB
002 GLMs For Binary Classification.mp4 83.0 MB
002 GLMs For Binary Classification_en.srt 10.1 KB
003 Common Algorithms For Supervised Classification.mp4 23.9 MB
003 Common Algorithms For Supervised Classification_en.srt 12.7 KB
004 Implement Random Forest For Binary Classification Problem.mp4 118.8 MB
004 Implement Random Forest For Binary Classification Problem_en.srt 11.5 KB
005 Measures of Accuracy_Binary Classification.mp4 58.1 MB
005 Measures of Accuracy_Binary Classification_en.srt 5.4 KB
006 Implement Random Forest For Multiple Classification Problem.mp4 86.3 MB
006 Implement Random Forest For Multiple Classification Problem_en.srt 9.9 KB
007 Gradient Boosting Machines (GBM) for Binary Classification.mp4 66.5 MB
007 Gradient Boosting Machines (GBM) for Binary Classification_en.srt 6.6 KB
001 A Brief Introduction to Artificial Intelligence.mp4 95.6 MB
001 A Brief Introduction to Artificial Intelligence_en.srt 10.3 KB
002 Theory Behind ANN and DNN.mp4 93.7 MB
002 Theory Behind ANN and DNN_en.srt 11.3 KB
003 Implement an ANN with H2o For Multi-Class Supervised Classification.mp4 109.2 MB
003 Implement an ANN with H2o For Multi-Class Supervised Classification_en.srt 11.0 KB
004 What Are Activation Functions_ Theory.mp4 86.8 MB
004 What Are Activation Functions_ Theory_en.srt 7.2 KB
005 Implement a DNN with H2o For Multi-Class Supervised Classification.mp4 61.3 MB
005 Implement a DNN with H2o For Multi-Class Supervised Classification_en.srt 7.2 KB
006 Implement a (Less Intensive) DNN with H2o For Supervised Classification.mp4 30.7 MB
006 Implement a (Less Intensive) DNN with H2o For Supervised Classification_en.srt 4.4 KB
007 Identify the Important Predictors.mp4 95.8 MB
007 Identify the Important Predictors_en.srt 8.3 KB
008 DNN For Regression.mp4 57.4 MB
008 DNN For Regression_en.srt 4.3 KB
001 Autoencoders for Unsupervised Learning.mp4 25.8 MB
001 Autoencoders for Unsupervised Learning_en.srt 2.2 KB
002 Unsupervised Classification with H2o.mp4 107.1 MB
002 Unsupervised Classification with H2o_en.srt 5.7 KB
003 More Autoencoders _ Credit Card Fraud Detection.mp4 55.5 MB
003 More Autoencoders _ Credit Card Fraud Detection_en.srt 4.1 KB
004 Use the Autoencoder Model for Anomaly Detection.mp4 68.1 MB
004 Use the Autoencoder Model for Anomaly Detection_en.srt 5.9 KB
Bonus Resources.txt 357 bytes

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