|
RAR-files |
| jgt-xgboost-python-scikit-learn-machine-learning.rar |
15,000,000 |
B370E8F9 |
| jgt-xgboost-python-scikit-learn-machine-learning.r00 |
15,000,000 |
63AFAEF1 |
| jgt-xgboost-python-scikit-learn-machine-learning.r01 |
15,000,000 |
4AE54F58 |
| jgt-xgboost-python-scikit-learn-machine-learning.r02 |
15,000,000 |
546C27A3 |
| jgt-xgboost-python-scikit-learn-machine-learning.r03 |
15,000,000 |
F8934241 |
| jgt-xgboost-python-scikit-learn-machine-learning.r04 |
15,000,000 |
82FC884A |
| jgt-xgboost-python-scikit-learn-machine-learning.r05 |
15,000,000 |
DAB5490B |
| jgt-xgboost-python-scikit-learn-machine-learning.r06 |
15,000,000 |
E7E3C881 |
| jgt-xgboost-python-scikit-learn-machine-learning.r07 |
9,792,024 |
8AAAE64C |
|
| Total size: |
129,792,024 |
|
|
| Archived
files |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\01.Course Overview\0101.Course Overview.mp4
[449ea9ff02130c9]
|
3,185,709 |
4567B7CF |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0201.Module Overview.mp4
[feb64e6cf89dc208]
|
2,240,930 |
D43B9411 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0202.Why Take This Course.mp4
[7f095dfdc392558]
|
1,328,439 |
B32EB6AB |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0203.Course Overview.mp4
[f49951241fa3a449]
|
2,386,507 |
31BBFCA3 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0204.Skills Recommended for This Course.mp4
[4ce1f4fba003ce6f]
|
1,571,723 |
439F022C |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0205.The Decision Tree.mp4
[e920da3f098bec79]
|
2,824,879 |
A3233670 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0206.Ensemble Boosting.mp4
[8df8129af924d494]
|
1,827,548 |
90414788 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0207.Gradient Boosting.mp4
[e5802cb92509f161]
|
2,062,257 |
DA578717 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0208.XGBoost Demonstration.mp4
[687df00f05e9a924]
|
6,011,542 |
3405381D |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes\0209.Summary.mp4
[52115cf1f485ae2d]
|
1,875,282 |
8B535438 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0301.Module Overview.mp4
[2382a8b6611d1a5d]
|
2,322,444 |
1C635E07 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0302.The AI Hierarchy.mp4
[20f5e1581884a726]
|
3,078,820 |
37D4FA6E |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0303.The Two Types of Learning.mp4
[b3d31f1ad12b7edb]
|
2,570,952 |
5E763327 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0304.The Array.mp4
[37e9beaafc13401d]
|
1,122,855 |
5246563D |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0305.Machine Learning Process.mp4
[5295a5da464a4506]
|
2,330,267 |
4E716DD8 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0306.Wrangling the dataset.mp4
[1806f7edb834fc13]
|
11,972,468 |
67088BA8 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting\0307.Summary.mp4
[87e877aae99635ce]
|
2,308,095 |
C98E908C |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0401.Module Overview.mp4
[12e116889ede1443]
|
2,523,990 |
0D438D52 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0402.Train Test Split.mp4
[4e867fc8cbdcd85]
|
1,534,638 |
DC52C477 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0403.Classification and Regression.mp4
[4611c077f136a401]
|
2,181,555 |
46733EA6 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0404.K-Fold Cross Validation.mp4
[88f9724f77d9d421]
|
2,331,314 |
F57A40D7 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0405.Early Stopping.mp4
[d6f26e51e7d22e86]
|
1,393,085 |
466A08CA |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0406.Validation and Scoring Demonstration.mp4
[6f3490f33b2daaf0]
|
12,178,877 |
755598B2 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models\0407.Summary.mp4
[44037028e4242075]
|
2,039,707 |
2D495200 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0501.Module Overview.mp4
[21a05a039a4c7de2]
|
1,907,086 |
5BD4F7C6 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0502.Serialization.mp4
[d53ee2f4fbdbc5a8]
|
968,988 |
14D6A6D2 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0503.Pickle.mp4
[8b7f35f43ff3b223]
|
1,885,844 |
76760C82 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0504.Saving to JSON.mp4
[ee6313bea0f3d7a3]
|
2,165,323 |
0F8D9B4D |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0505.Pickle Problems.mp4
[7d5ae2944c17df39]
|
1,821,856 |
78686623 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0506.Saving Models to Disk Demonstration.mp4
[27d442d064c0b547]
|
10,712,995 |
A6554575 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model\0507.Summary.mp4
[aec0fd98d386278e]
|
1,616,561 |
EBFAB22B |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0601.Module Overview.mp4
[37e92d369b517d6d]
|
1,924,263 |
419F9319 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0602.Feature Selection Defined.mp4
[36a6f6fb41a04fdb]
|
1,990,428 |
2793FE7A |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0603.Feature Selection Algorithms.mp4
[67b0d824bf78f4ec]
|
2,513,798 |
B500D803 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0604.Feature Importance.mp4
[90b429a468f32de3]
|
2,234,382 |
2161AE4C |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0605.Feature Construction .mp4
[c103d28678d81f1]
|
2,726,996 |
D873BA1A |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0606.Feature Selection Demonstration.mp4
[4f5af6f250834e39]
|
10,041,341 |
2E03217D |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0607.Summary.mp4
[13780389c94a5e3a]
|
2,321,673 |
4FEAC9BA |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting\0608.Course Summary.mp4
[3892bb40e80a49e1]
|
5,256,585 |
8E703612 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\Exercise Files\xgboost-python-scikit-learn-machine-learning.zip |
3,204,127 |
872934DC |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\01.Course Overview |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\02.Introducing Essential Processes |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\03.Preparing Data for Gradient Boosting |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\04.Scoring XGBoost Models |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\05.Saving the Trained Model |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\06.Selecting Features in Gradient Boosting |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python\Exercise Files |
0 |
00000000 |
|
Pluralsight Machine Learning with XGBoost Using Scikit-learn in Python |
0 |
00000000 |
|
| Total size: |
128,496,129 |
|
|
