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Checked by:
Category:
Language:
English
Total Size:
3.0 GB
Info Hash:
C420B4EBBA273AA8F8CEB60924A185C607977C00
Added By:
Added:
Oct. 24, 2023, 3:13 p.m.
Stats:
|
(Last updated: May 15, 2025, 4:47 p.m.)
| File | Size |
|---|---|
| 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 |
Name
DL
Uploader
Size
S/L
Added
-
713.3 MB
[0
/
3]
2025-02-21
| Uploaded by freecoursewb | Size 713.3 MB | Health [ 0 /3 ] | Added 2025-02-21 |
-
1.8 GB
[72
/
10]
2025-01-13
| Uploaded by FreeCourseWeb | Size 1.8 GB | Health [ 72 /10 ] | Added 2025-01-13 |
NOTE
SOURCE: Udemy Complete Machine Learning and Deep Learning With H2O in R
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