|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
13б |
| 1 |
42б |
| 1. [Hands On] - Data Input-Output - Upload Data.mp4 |
18.57Мб |
| 1. [Hands On] - Data Input-Output - Upload Data.srt |
8.24Кб |
| 1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp4 |
21.92Мб |
| 1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.srt |
9.94Кб |
| 1.1 Employee Dataset - Full.csv |
1.85Кб |
| 1.1 Links for datasets.pdf |
261.42Кб |
| 1. An Important Note..html |
347б |
| 1. A note on Anaconda and Spyder..html |
767б |
| 1. Azure ML Webservice - Prepare the experiment for webservice.mp4 |
5.56Мб |
| 1. Azure ML Webservice - Prepare the experiment for webservice.srt |
2.63Кб |
| 1. DP-100 Exam Curriculum.mp4 |
61.19Мб |
| 1. DP-100 Exam Curriculum.srt |
9.82Кб |
| 1. Feature Selection - Section Introduction.mp4 |
7.73Мб |
| 1. Feature Selection - Section Introduction.srt |
7.12Кб |
| 1. Introduction to AzureML SDK.mp4 |
26.86Мб |
| 1. Introduction to AzureML SDK.srt |
4.75Кб |
| 1. Logistic Regression - What is Logistic Regression.mp4 |
11.48Мб |
| 1. Logistic Regression - What is Logistic Regression.srt |
6.69Кб |
| 1. Section Introduction.mp4 |
5.41Мб |
| 1. Section Introduction.srt |
3.15Кб |
| 1. To be Added.html |
103б |
| 1. To be Added.html |
103б |
| 1. Understand the AzureMLService Architecture.mp4 |
34.91Мб |
| 1. Understand the AzureMLService Architecture.srt |
8.97Кб |
| 1. Way Forward.mp4 |
57.00Мб |
| 1. Way Forward.srt |
5.62Кб |
| 1. What is a Recommendation System.mp4 |
34.96Мб |
| 1. What is a Recommendation System.srt |
16.74Кб |
| 1. What is Cloud Computing.mp4 |
34.84Мб |
| 1. What is Cloud Computing.srt |
8.81Кб |
| 1. What is Cluster Analysis.mp4 |
22.38Мб |
| 1. What is Cluster Analysis.srt |
11.17Кб |
| 1. What is Linear Regression.mp4 |
14.03Мб |
| 1. What is Linear Regression.srt |
5.98Кб |
| 1. What is Text Analytics or Natural Language Processing.mp4 |
40.70Мб |
| 1. What is Text Analytics or Natural Language Processing.srt |
8.35Кб |
| 1. What You Will Learn in This Section.mp4 |
12.39Мб |
| 1. What You Will Learn in This Section.mp4 |
4.34Мб |
| 1. What You Will Learn in This Section.srt |
2.65Кб |
| 1. What You Will Learn in This Section.srt |
2.40Кб |
| 10 |
866.52Кб |
| 10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp4 |
17.28Мб |
| 10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.srt |
6.53Кб |
| 10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4 |
25.16Мб |
| 10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.srt |
10.27Кб |
| 10.1 050 - Summarise the data.py |
546б |
| 10.1 160 - Run a script and Log metrics.py |
1.74Кб |
| 10.1 50 - Lists.py |
900б |
| 10.1 Bank Telemarketing.csv |
4.70Мб |
| 10. Basics of Machine Learning.html |
140б |
| 10. Create Summary Statistics using describe.mp4 |
71.56Мб |
| 10. Create Summary Statistics using describe.srt |
7.40Кб |
| 10. Data Normalization - Scale and Reduce.mp4 |
5.33Мб |
| 10. Data Normalization - Scale and Reduce.srt |
3.03Кб |
| 10. Python Lists - Operations Part 1.mp4 |
25.08Мб |
| 10. Python Lists - Operations Part 1.srt |
3.91Кб |
| 10. Run a sample experiment using AzureML SDK - Part 1.mp4 |
61.73Мб |
| 10. Run a sample experiment using AzureML SDK - Part 1.srt |
7.97Кб |
| 10. Understanding the AzureML Compute Resources.mp4 |
40.77Мб |
| 10. Understanding the AzureML Compute Resources.srt |
8.26Кб |
| 100 |
145.76Кб |
| 101 |
274.82Кб |
| 102 |
549.06Кб |
| 103 |
552.61Кб |
| 104 |
612.29Кб |
| 105 |
669.02Кб |
| 106 |
98.12Кб |
| 107 |
857.71Кб |
| 108 |
943.66Кб |
| 109 |
788.92Кб |
| 11 |
522.38Кб |
| 11. [Hands On] - Data Normalization.mp4 |
5.89Мб |
| 11. [Hands On] - Data Normalization.srt |
2.47Кб |
| 11.1 060 - Outlier Detection and clipping.py |
725б |
| 11. Clip Values - Remove Outliers using Constants.mp4 |
41.85Мб |
| 11. Clip Values - Remove Outliers using Constants.srt |
5.65Кб |
| 11. Create a Compute Cluster and Compute Instance.mp4 |
48.55Мб |
| 11. Create a Compute Cluster and Compute Instance.srt |
6.84Кб |
| 11. Decision Forest - Parameters Explained.mp4 |
5.79Мб |
| 11. Decision Forest - Parameters Explained.srt |
3.88Кб |
| 11. Python Lists - Operations Part 2.mp4 |
17.31Мб |
| 11. Python Lists - Operations Part 2.srt |
2.17Кб |
| 11. Regression Analysis.html |
141б |
| 11. Run a sample experiment using AzureML SDK - Part 2.mp4 |
94.15Мб |
| 11. Run a sample experiment using AzureML SDK - Part 2.srt |
10.11Кб |
| 110 |
939.78Кб |
| 111 |
305.97Кб |
| 112 |
633.46Кб |
| 113 |
945.00Кб |
| 114 |
79.27Кб |
| 115 |
251.26Кб |
| 116 |
351.13Кб |
| 117 |
470.57Кб |
| 118 |
983.94Кб |
| 119 |
435.76Кб |
| 12 |
979.16Кб |
| 12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4 |
35.09Мб |
| 12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.srt |
14.52Кб |
| 12. Clip Values - Remove Outliers with Percentiles.mp4 |
72.63Мб |
| 12. Clip Values - Remove Outliers with Percentiles.srt |
7.38Кб |
| 12. Multidimensional Lists in Python.mp4 |
27.45Мб |
| 12. Multidimensional Lists in Python.srt |
4.23Кб |
| 12. PCA - What is PCA and Curse of Dimensionality.mp4 |
10.73Мб |
| 12. PCA - What is PCA and Curse of Dimensionality.srt |
6.39Кб |
| 12. Run a script in Azureml environment - Part 1.mp4 |
27.63Мб |
| 12. Run a script in Azureml environment - Part 1.srt |
4.62Кб |
| 12. What is an AzureML Pipeline.mp4 |
26.46Мб |
| 12. What is an AzureML Pipeline.srt |
6.06Кб |
| 120 |
444.38Кб |
| 121 |
525.33Кб |
| 122 |
657.12Кб |
| 123 |
924.17Кб |
| 124 |
354.15Кб |
| 125 |
569.35Кб |
| 126 |
704.04Кб |
| 127 |
732.18Кб |
| 128 |
434.08Кб |
| 129 |
475.14Кб |
| 13 |
11.98Кб |
| 13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp4 |
18.57Мб |
| 13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.srt |
8.22Кб |
| 13. [Hands On] - Principal Component Analysis.mp4 |
7.40Мб |
| 13. [Hands On] - Principal Component Analysis.srt |
3.71Кб |
| 13.1 070 - Write to a delimited file.py |
417б |
| 13.1 IRIS Dataset Link.txt |
74б |
| 13. Convert and Save a delimited file using Pandas.mp4 |
71.74Мб |
| 13. Convert and Save a delimited file using Pandas.srt |
7.00Кб |
| 13. Create a Pipeline using AzureML Designer.mp4 |
94.35Мб |
| 13. Create a Pipeline using AzureML Designer.srt |
11.60Кб |
| 13. Run a script in Azureml environment - Part 2.mp4 |
67.72Мб |
| 13. Run a script in Azureml environment - Part 2.srt |
6.67Кб |
| 13. Slicing a multidimensional list.mp4 |
41.75Мб |
| 13. Slicing a multidimensional list.srt |
5.77Кб |
| 130 |
79.47Кб |
| 131 |
474.71Кб |
| 132 |
745.60Кб |
| 133 |
911.41Кб |
| 134 |
79.43Кб |
| 135 |
107.47Кб |
| 136 |
456.63Кб |
| 137 |
686.85Кб |
| 138 |
755.66Кб |
| 139 |
804.41Кб |
| 14 |
485.77Кб |
| 14.1 080 - Normalize the data.py |
792б |
| 14.1 60 - tuples.py |
137б |
| 14. Data Normalization.mp4 |
122.41Мб |
| 14. Data Normalization.srt |
11.35Кб |
| 14. Join Data - Join Multiple Datasets based on common keys.mp4 |
10.48Мб |
| 14. Join Data - Join Multiple Datasets based on common keys.srt |
6.26Кб |
| 14. Python Tuples.mp4 |
13.67Мб |
| 14. Python Tuples.srt |
3.80Кб |
| 14. Run a script in Azureml environment - Part 3.mp4 |
81.78Мб |
| 14. Run a script in Azureml environment - Part 3.srt |
8.14Кб |
| 14. Submit the Designer Pipeline run.mp4 |
89.94Мб |
| 14. Submit the Designer Pipeline run.srt |
11.65Кб |
| 14. SVM - What is Support Vector Machine.mp4 |
14.90Мб |
| 14. SVM - What is Support Vector Machine.srt |
3.65Кб |
| 140 |
989.55Кб |
| 141 |
90.35Кб |
| 142 |
178.29Кб |
| 143 |
237.61Кб |
| 144 |
335.24Кб |
| 145 |
396.24Кб |
| 146 |
703.09Кб |
| 147 |
795.40Кб |
| 148 |
834.05Кб |
| 149 |
897.62Кб |
| 15 |
57.70Кб |
| 15. [Hands On] - Join Data - Experiment.mp4 |
15.11Мб |
| 15. [Hands On] - Join Data - Experiment.srt |
2.77Кб |
| 15. [Hands On] - SVM - Adult Census Income Prediction.mp4 |
13.83Мб |
| 15. [Hands On] - SVM - Adult Census Income Prediction.srt |
5.72Кб |
| 15.1 090 - Label encoding.py |
743б |
| 15.1 EmpDeptJC.csv |
108б |
| 15.2 EmpSalaryJC.csv |
110б |
| 15. Create an Inference Pipeline.mp4 |
66.71Мб |
| 15. Create an Inference Pipeline.srt |
8.56Кб |
| 15. Label Encoding of String Categorical data.mp4 |
84.99Мб |
| 15. Label Encoding of String Categorical data.srt |
9.00Кб |
| 15. Python Dictionary.mp4 |
14.26Мб |
| 15. Python Dictionary.srt |
3.60Кб |
| 15. Run a script in Azureml environment - Part 4.mp4 |
59.64Мб |
| 15. Run a script in Azureml environment - Part 4.srt |
7.94Кб |
| 150 |
961.91Кб |
| 151 |
93.18Кб |
| 152 |
98.09Кб |
| 153 |
350.17Кб |
| 154 |
423.65Кб |
| 155 |
626.05Кб |
| 156 |
745.44Кб |
| 157 |
779.91Кб |
| 158 |
848.50Кб |
| 159 |
926.82Кб |
| 16 |
142.25Кб |
| 16.1 70 - dictionary.py |
812б |
| 16. Classification Quiz.html |
140б |
| 16. Deploy a real-time endpoint using Designer.mp4 |
66.57Мб |
| 16. Deploy a real-time endpoint using Designer.srt |
9.46Кб |
| 16. Python Dictionary Hands on Part 1.mp4 |
32.05Мб |
| 16. Python Dictionary Hands on Part 1.srt |
4.49Кб |
| 16. Run a script in Azureml environment - Part 5.mp4 |
56.58Мб |
| 16. Run a script in Azureml environment - Part 5.srt |
6.31Кб |
| 16. Why Hot encoding is required.mp4 |
12.66Мб |
| 16. Why Hot encoding is required.srt |
3.50Кб |
| 160 |
277.25Кб |
| 161 |
309.94Кб |
| 162 |
520.34Кб |
| 163 |
528.19Кб |
| 164 |
760.29Кб |
| 165 |
56.56Кб |
| 166 |
149.17Кб |
| 167 |
244.62Кб |
| 168 |
275.42Кб |
| 169 |
527.62Кб |
| 17 |
695.95Кб |
| 17.1 100 - Create Dummy Variables.py |
341б |
| 17. Create a batch inference pipeline using Designer.mp4 |
61.84Мб |
| 17. Create a batch inference pipeline using Designer.srt |
8.34Кб |
| 17. DP-100 Exam Coverage So far..mp4 |
13.91Мб |
| 17. DP-100 Exam Coverage So far..srt |
2.40Кб |
| 17. Hot Encoding using Pandas get_dummies.mp4 |
37.65Мб |
| 17. Hot Encoding using Pandas get_dummies.srt |
4.12Кб |
| 17. Python Dictionary Hands on Part 2.mp4 |
30.79Мб |
| 17. Python Dictionary Hands on Part 2.srt |
3.94Кб |
| 170 |
700.87Кб |
| 171 |
839.59Кб |
| 172 |
733.48Кб |
| 173 |
880.43Кб |
| 174 |
276.37Кб |
| 175 |
618.65Кб |
| 176 |
990.51Кб |
| 177 |
299.36Кб |
| 178 |
309.74Кб |
| 179 |
615.62Кб |
| 18 |
1014.58Кб |
| 18.1 110 - Split Data.py |
903б |
| 18. Python Functions.mp4 |
16.54Мб |
| 18. Python Functions.srt |
5.32Кб |
| 18. Run a Batch Inference Pipeline from Designer.mp4 |
42.12Мб |
| 18. Run a Batch Inference Pipeline from Designer.srt |
4.94Кб |
| 18. Split The Data for training and testing.mp4 |
107.48Мб |
| 18. Split The Data for training and testing.srt |
11.02Кб |
| 18. Train and Run a Model Script in AzureML Part 1.mp4 |
45.38Мб |
| 18. Train and Run a Model Script in AzureML Part 1.srt |
6.26Кб |
| 180 |
640.04Кб |
| 181 |
642.84Кб |
| 182 |
110.21Кб |
| 183 |
219.52Кб |
| 184 |
449.02Кб |
| 185 |
604.88Кб |
| 186 |
685.67Кб |
| 187 |
306.77Кб |
| 188 |
318.02Кб |
| 189 |
673.56Кб |
| 19 |
46.87Кб |
| 19.1 80 - Functions.py |
550б |
| 19. Build Logistic Regression using Python - Part 1.mp4 |
44.07Мб |
| 19. Build Logistic Regression using Python - Part 1.srt |
4.33Кб |
| 19. Python Functions - Hands on.mp4 |
33.67Мб |
| 19. Python Functions - Hands on.srt |
4.76Кб |
| 19. Train and Run a Model Script in AzureML Part 2.mp4 |
90.53Мб |
| 19. Train and Run a Model Script in AzureML Part 2.srt |
9.19Кб |
| 190 |
695.00Кб |
| 191 |
616.60Кб |
| 192 |
926.62Кб |
| 193 |
53.74Кб |
| 194 |
168.66Кб |
| 195 |
186.60Кб |
| 196 |
330.15Кб |
| 197 |
772.44Кб |
| 198 |
990.46Кб |
| 199 |
160.40Кб |
| 2 |
26б |
| 2. [Hands On] - Cluster Analysis Experiment 1.mp4 |
30.92Мб |
| 2. [Hands On] - Cluster Analysis Experiment 1.srt |
13.72Кб |
| 2. [Hands On] - Data Input-Output - Convert and Unpack.mp4 |
22.08Мб |
| 2. [Hands On] - Data Input-Output - Convert and Unpack.srt |
9.34Кб |
| 2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp4 |
9.18Мб |
| 2. [Hands On] - Deploy Machine Learning Model As a Web Service.srt |
3.61Кб |
| 2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4 |
52.20Мб |
| 2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.srt |
22.86Кб |
| 2.1 100 - Create Workspace and write config.py |
860б |
| 2.1 Callcenter Data.csv |
831б |
| 2.1 Employee Dataset - Full.zip |
773б |
| 2.1 Loan Approval Prediction.csv |
37.12Кб |
| 2. Bonus Lecture.html |
6.97Кб |
| 2. Create AzureML Workspace using SDK.mp4 |
63.77Мб |
| 2. Create AzureML Workspace using SDK.srt |
8.37Кб |
| 2. Create the AzureML Workspace.mp4 |
76.36Мб |
| 2. Create the AzureML Workspace.srt |
10.34Кб |
| 2. Data Preparation using Recommender Split.mp4 |
14.92Мб |
| 2. Data Preparation using Recommender Split.srt |
8.30Кб |
| 2. How to Summarize Data.mp4 |
11.70Мб |
| 2. How to Summarize Data.srt |
6.39Кб |
| 2. Hyperparameter Tuning.html |
140б |
| 2. Install Anaconda.mp4 |
41.69Мб |
| 2. Install Anaconda.srt |
5.72Кб |
| 2. Note on DP-100 Exam and New Studio.mp4 |
29.36Мб |
| 2. Note on DP-100 Exam and New Studio.srt |
5.31Кб |
| 2. Pearson Correlation Coefficient.mp4 |
47.22Мб |
| 2. Pearson Correlation Coefficient.srt |
7.51Кб |
| 2. Regression Analysis - Common Metrics.mp4 |
12.59Мб |
| 2. Regression Analysis - Common Metrics.srt |
6.35Кб |
| 2. Text Pre-Processing.mp4 |
54.61Мб |
| 2. Text Pre-Processing.srt |
15.18Кб |
| 2. What is Azure.mp4 |
18.10Мб |
| 2. What is Azure.srt |
4.34Кб |
| 2. What is Azure ML and high level architecture..mp4 |
7.03Мб |
| 2. What is Azure ML and high level architecture..srt |
3.92Кб |
| 2. What this section is about.mp4 |
10.76Мб |
| 2. What this section is about.srt |
2.40Кб |
| 20 |
14.44Кб |
| 20.1 120 - Logistic regression.py |
2.07Кб |
| 20.2 defaults.csv |
139.06Кб |
| 20. Build Logistic Regression using Python - Part 2.mp4 |
117.21Мб |
| 20. Build Logistic Regression using Python - Part 2.srt |
11.44Кб |
| 20. Global Vs Local Variables in Python.mp4 |
49.60Мб |
| 20. Global Vs Local Variables in Python.srt |
8.50Кб |
| 20. Train and Run a Model Script in AzureML Part 3.mp4 |
110.03Мб |
| 20. Train and Run a Model Script in AzureML Part 3.srt |
9.73Кб |
| 200 |
467.28Кб |
| 201 |
611.67Кб |
| 202 |
830.42Кб |
| 203 |
898.80Кб |
| 21 |
578.23Кб |
| 21. Train and Run a Model Script in AzureML Part 4.mp4 |
46.44Мб |
| 21. Train and Run a Model Script in AzureML Part 4.srt |
4.80Кб |
| 21. Types of Function Arguments.mp4 |
15.27Мб |
| 21. Types of Function Arguments.srt |
4.49Кб |
| 22 |
664.43Кб |
| 22.1 88 - Required Arguments.py |
520б |
| 22. Function Arguments - Required Arguments.mp4 |
52.39Мб |
| 22. Function Arguments - Required Arguments.srt |
7.01Кб |
| 22. Train and Run a Model Script in AzureML Part 5.mp4 |
92.49Мб |
| 22. Train and Run a Model Script in AzureML Part 5.srt |
8.90Кб |
| 23 |
788.25Кб |
| 23.1 210 - Provision Compute Cluster.py |
813б |
| 23.1 90 - Default Arguments.py |
571б |
| 23. Function Arguments - Default Arguments.mp4 |
41.81Мб |
| 23. Function Arguments - Default Arguments.srt |
5.92Кб |
| 23. Provisioning Compute Cluster using SDK.mp4 |
86.95Мб |
| 23. Provisioning Compute Cluster using SDK.srt |
10.75Кб |
| 24 |
220.48Кб |
| 24.1 92 - Keyword Arguments.py |
532б |
| 24. Automate Model Training using AzureML SDK.mp4 |
34.67Мб |
| 24. Automate Model Training using AzureML SDK.srt |
8.24Кб |
| 24. Function Arguments - Keyword Arguments.mp4 |
49.00Мб |
| 24. Function Arguments - Keyword Arguments.srt |
7.65Кб |
| 25 |
283.58Кб |
| 25. Automate Model Training - Define Pipeline Steps.mp4 |
89.32Мб |
| 25. Automate Model Training - Define Pipeline Steps.srt |
13.29Кб |
| 25. Object Oriented Programming.mp4 |
53.00Мб |
| 25. Object Oriented Programming.srt |
11.85Кб |
| 26 |
233.50Кб |
| 26.1 95 - class and objects.py |
593б |
| 26. Automate Model Training - Define Run Configuration.mp4 |
69.75Мб |
| 26. Automate Model Training - Define Run Configuration.srt |
7.03Кб |
| 26. Define a Class and Create an Object.mp4 |
79.77Мб |
| 26. Define a Class and Create an Object.srt |
15.15Кб |
| 27 |
653.65Кб |
| 27.1 220 - Pipeline Job.py |
3.35Кб |
| 27. Automate Model Training - Define Build and Run.mp4 |
45.57Мб |
| 27. Automate Model Training - Define Build and Run.srt |
5.45Кб |
| 27. Initialize the Class Attributes using __init__.mp4 |
63.89Мб |
| 27. Initialize the Class Attributes using __init__.srt |
8.65Кб |
| 28 |
845.89Кб |
| 28. Detour - Command Line Arguments.mp4 |
72.73Мб |
| 28. Detour - Command Line Arguments.srt |
10.81Кб |
| 28. Packages and Modules in Python.mp4 |
26.35Мб |
| 28. Packages and Modules in Python.srt |
5.96Кб |
| 29 |
888.61Кб |
| 29.1 220 - Dataprep Pipeline.py |
2.00Кб |
| 29. Automate Model Training - Create Dataprep Step.mp4 |
109.63Мб |
| 29. Automate Model Training - Create Dataprep Step.srt |
12.42Кб |
| 3 |
24б |
| 3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4 |
18.36Мб |
| 3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.srt |
7.63Кб |
| 3. [Hands On] - Data Input-Output - Import Data.mp4 |
13.12Мб |
| 3. [Hands On] - Data Input-Output - Import Data.srt |
6.59Кб |
| 3. [Hands On] - Linear Regression model using OLS.mp4 |
91.04Мб |
| 3. [Hands On] - Linear Regression model using OLS.srt |
11.26Кб |
| 3. [Hands On] - Summarize Data - Experiment.mp4 |
8.14Мб |
| 3. [Hands On] - Summarize Data - Experiment.srt |
3.25Кб |
| 3. [Hands On] - Use the Web Service - Example of Excel.mp4 |
16.58Мб |
| 3. [Hands On] - Use the Web Service - Example of Excel.srt |
7.02Кб |
| 3.1 010 - Pandas part 1.py |
1.08Кб |
| 3.10 Section 10 - Feature Selection.pdf |
2.95Мб |
| 3.11 Section 04 - Classification - 003 - SVM.pdf |
1.12Мб |
| 3.12 Section 04 - Classification - 002 - Decision Tree.pdf |
3.40Мб |
| 3.13 Section 11 - Recommendation System.pdf |
3.10Мб |
| 3.14 Section - Text Analytics.pdf |
2.03Мб |
| 3.15 Section 05 - Tune Hyperparameter.pdf |
1.19Мб |
| 3.1 Adult Dataset URL.txt |
74б |
| 3.1 Section 02 - Getting Started with AzureML.pdf |
2.68Мб |
| 3.2 Section 08 - Clustering.pdf |
1.54Мб |
| 3.3 Section 06 - Deploy Webservice.pdf |
702.45Кб |
| 3.4 Section 04 - Classification - 001 - Logistic Regression.pdf |
1.40Мб |
| 3.5 Section 09 - Data Processing.pdf |
2.84Мб |
| 3.6 All Data Files.zip |
632.49Кб |
| 3.7 Section 01 - Basics of Machine Learning.pdf |
1.84Мб |
| 3.8 Section 07 - Regression.pdf |
2.82Мб |
| 3.9 Section 03 - Data Pre-processing.pdf |
1.01Мб |
| 3. Azure Basic Terms and Concepts.mp4 |
24.23Мб |
| 3. Azure Basic Terms and Concepts.srt |
5.46Кб |
| 3. Bag Of Words and N-Gram Models for Text features.mp4 |
49.96Мб |
| 3. Bag Of Words and N-Gram Models for Text features.srt |
8.59Кб |
| 3. Chi Square Test of Independence.mp4 |
8.28Мб |
| 3. Chi Square Test of Independence.srt |
6.22Кб |
| 3. Creating a Free Azure ML Account.mp4 |
23.70Мб |
| 3. Creating a Free Azure ML Account.srt |
3.66Кб |
| 3. Hello World and Know your environment.mp4 |
29.64Мб |
| 3. Hello World and Know your environment.srt |
5.58Кб |
| 3. Logistic Regression - Understand Parameters and Their Impact.mp4 |
19.54Мб |
| 3. Logistic Regression - Understand Parameters and Their Impact.srt |
12.96Кб |
| 3. Pandas - Import Data for Experiments.mp4 |
70.57Мб |
| 3. Pandas - Import Data for Experiments.srt |
7.00Кб |
| 3. The course slides as well as Data Files for all sections.html |
362б |
| 3. Verify the Workspace and Write the Workspace Config File.mp4 |
26.73Мб |
| 3. Verify the Workspace and Write the Workspace Config File.srt |
3.29Кб |
| 3. View and Manage Workspace Settings.mp4 |
44.43Мб |
| 3. View and Manage Workspace Settings.srt |
5.57Кб |
| 3. What is Matchbox Recommender and Train Matchbox Recommender.mp4 |
14.55Мб |
| 3. What is Matchbox Recommender and Train Matchbox Recommender.srt |
8.28Кб |
| 30 |
1016.64Кб |
| 30.1 220 - Training Pipeline.py |
3.00Кб |
| 30. Automate Model Training - Create Training Step.mp4 |
36.28Мб |
| 30. Automate Model Training - Create Training Step.srt |
3.44Кб |
| 31 |
277.92Кб |
| 31. Run the pipeline and see the results.mp4 |
83.23Мб |
| 31. Run the pipeline and see the results.srt |
9.97Кб |
| 32 |
374.89Кб |
| 33 |
267.87Кб |
| 34 |
448.77Кб |
| 35 |
443.05Кб |
| 36 |
257.89Кб |
| 37 |
292.32Кб |
| 38 |
288.26Кб |
| 39 |
299.29Кб |
| 4 |
11б |
| 4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4 |
26.46Мб |
| 4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.srt |
12.00Кб |
| 4. [Hands On] - Linear Regression - R Squared.mp4 |
10.32Мб |
| 4. [Hands On] - Linear Regression - R Squared.srt |
4.36Кб |
| 4.1 004 - Logistic Regression - Understanding the results.xlsx |
23.96Кб |
| 4.1 110 - Create Datastore.py |
918б |
| 4.1 Employee Dataset - TSV.txt |
1.85Кб |
| 4.2 Employee Dataset - AR2.csv |
1.34Кб |
| 4.3 Employee Dataset - AC1.csv |
1.62Кб |
| 4.4 Employee Dataset - AC2.csv |
260б |
| 4.5 Employee Dataset - AR1.csv |
672б |
| 4. Azure ML Studio Overview and walk-through.mp4 |
12.17Мб |
| 4. Azure ML Studio Overview and walk-through.srt |
5.18Кб |
| 4. AzureML Web Service.html |
141б |
| 4. Azure Storage and Data Resource.mp4 |
34.37Мб |
| 4. Azure Storage and Data Resource.srt |
9.70Кб |
| 4. Clustering or Cluster Analysis.html |
141б |
| 4. Create and Register a Datastore using AzureML SDK.mp4 |
101.25Мб |
| 4. Create and Register a Datastore using AzureML SDK.srt |
10.12Кб |
| 4. Feature Hashing.mp4 |
75.17Мб |
| 4. Feature Hashing.srt |
14.55Кб |
| 4. How to Score the Matchbox Recommender.mp4 |
10.94Мб |
| 4. How to Score the Matchbox Recommender.srt |
5.93Кб |
| 4. Important Message About Udemy Reviews.mp4 |
4.69Мб |
| 4. Important Message About Udemy Reviews.srt |
4.18Кб |
| 4. Kendall Correlation Coefficient.mp4 |
6.70Мб |
| 4. Kendall Correlation Coefficient.srt |
4.58Кб |
| 4. Outliers Treatment - Clip Values.mp4 |
11.49Мб |
| 4. Outliers Treatment - Clip Values.srt |
6.67Кб |
| 4. Overview of New AzureML Studio.mp4 |
83.35Мб |
| 4. Overview of New AzureML Studio.srt |
11.19Кб |
| 4. Pandas - Import Data Part 2.mp4 |
50.42Мб |
| 4. Pandas - Import Data Part 2.srt |
5.06Кб |
| 4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4 |
29.41Мб |
| 4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.srt |
13.64Кб |
| 4. Variable Types in Python.mp4 |
54.74Мб |
| 4. Variable Types in Python.srt |
8.98Кб |
| 40 |
436.00Кб |
| 41 |
115.33Кб |
| 42 |
240.39Кб |
| 43 |
162.43Кб |
| 44 |
281.31Кб |
| 45 |
827.21Кб |
| 46 |
878.56Кб |
| 47 |
1015.12Кб |
| 48 |
370.08Кб |
| 49 |
2.78Кб |
| 5 |
333.76Кб |
| 5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4 |
38.91Мб |
| 5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.srt |
18.71Кб |
| 5. [Hands On] - Outliers Treatment - Clip Values.mp4 |
17.65Мб |
| 5. [Hands On] - Outliers Treatment - Clip Values.srt |
7.46Кб |
| 5. [Hands On] - Restaurant Recommendation Experiment.mp4 |
36.18Мб |
| 5. [Hands On] - Restaurant Recommendation Experiment.srt |
13.13Кб |
| 5.1 120 - Create and register a dataset.py |
1.17Кб |
| 5.1 20 - Conditional Statements.py |
441б |
| 5.1 SQL Statement - Wine.txt |
141б |
| 5.2 Wine Quality Dataset.csv |
83.73Кб |
| 5. Azure ML Experiment Workflow.mp4 |
13.22Мб |
| 5. Azure ML Experiment Workflow.srt |
7.63Кб |
| 5. Azure Storage hands on.mp4 |
97.55Мб |
| 5. Azure Storage hands on.srt |
12.06Кб |
| 5. Conditional Statements in Python.mp4 |
33.39Мб |
| 5. Conditional Statements in Python.srt |
5.77Кб |
| 5. Create and Register a Dataset using SDK.mp4 |
96.07Мб |
| 5. Create and Register a Dataset using SDK.srt |
10.31Кб |
| 5. DP-100 Exam Coverage So far..mp4 |
11.73Мб |
| 5. DP-100 Exam Coverage So far..srt |
1.89Кб |
| 5. Gradient Descent.mp4 |
27.66Мб |
| 5. Gradient Descent.srt |
10.39Кб |
| 5. Logistic Regression - Model Selection and Impact Analysis.mp4 |
13.77Мб |
| 5. Logistic Regression - Model Selection and Impact Analysis.srt |
5.78Кб |
| 5. Note for the next Hands On..html |
200б |
| 5. Select Columns using Pandas.mp4 |
80.72Мб |
| 5. Select Columns using Pandas.srt |
7.50Кб |
| 5. Spearman's Rank Correlation.mp4 |
6.37Мб |
| 5. Spearman's Rank Correlation.srt |
4.10Кб |
| 5. Why Machine Learning is the Future.mp4 |
68.71Мб |
| 5. Why Machine Learning is the Future.srt |
10.46Кб |
| 50 |
426.33Кб |
| 51 |
266.11Кб |
| 52 |
402.83Кб |
| 53 |
1019.65Кб |
| 54 |
623.57Кб |
| 55 |
815.84Кб |
| 56 |
589.44Кб |
| 57 |
38.86Кб |
| 58 |
407.82Кб |
| 59 |
4.40Кб |
| 6 |
460.23Кб |
| 6. [Hands On] - Classify Customer Complaints using Text Analytics.mp4 |
90.99Мб |
| 6. [Hands On] - Classify Customer Complaints using Text Analytics.srt |
10.98Кб |
| 6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp4 |
13.19Мб |
| 6. [Hands On] - Comparison Experiment for Correlation Coefficients.srt |
8.12Кб |
| 6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4 |
19.66Мб |
| 6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.srt |
8.68Кб |
| 6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4 |
35.52Мб |
| 6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.srt |
16.82Кб |
| 6.1 130 - Access Workspace Datastore and Dataset.py |
1.52Кб |
| 6.1 ml_studio_overview_v1.1.pdf |
2.25Мб |
| 6.1 two-class complaints modified.txt |
47.39Кб |
| 6.1 winequality-red.csv |
83.73Кб |
| 6.2 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf |
404.10Кб |
| 6. Access Workspace, Datastore and Datasets using SDK.mp4 |
110.73Мб |
| 6. Access Workspace, Datastore and Datasets using SDK.srt |
10.43Кб |
| 6. Azure ComputeVirtual Machines.mp4 |
21.75Мб |
| 6. Azure ComputeVirtual Machines.srt |
4.34Кб |
| 6. Azure ML Cheat Sheet for Model Selection.mp4 |
11.26Мб |
| 6. Azure ML Cheat Sheet for Model Selection.srt |
6.64Кб |
| 6. Clean Missing Data with MICE.mp4 |
13.06Мб |
| 6. Clean Missing Data with MICE.srt |
7.00Кб |
| 6. Linear Regression Online Gradient Descent.mp4 |
6.71Мб |
| 6. Linear Regression Online Gradient Descent.srt |
2.22Кб |
| 6. Python Loops explained..mp4 |
12.27Мб |
| 6. Python Loops explained..srt |
2.68Кб |
| 6. Select Columns By drop method.mp4 |
74.01Мб |
| 6. Select Columns By drop method.srt |
7.52Кб |
| 6. Understanding the Matchbox Recommendation Results.mp4 |
17.44Мб |
| 6. Understanding the Matchbox Recommendation Results.srt |
8.27Кб |
| 6. What is AzureML Datastore and Dataset.mp4 |
31.37Мб |
| 6. What is AzureML Datastore and Dataset.srt |
7.13Кб |
| 6. What is Machine Learning.mp4 |
18.49Мб |
| 6. What is Machine Learning.srt |
11.13Кб |
| 60 |
459.64Кб |
| 61 |
796.68Кб |
| 62 |
576.50Кб |
| 63 |
444.39Кб |
| 64 |
630.92Кб |
| 65 |
583.62Кб |
| 66 |
947.94Кб |
| 67 |
906.11Кб |
| 68 |
157.97Кб |
| 69 |
193.80Кб |
| 7 |
967.74Кб |
| 7. [Hands On] - Clean Missing Data with MICE.mp4 |
15.92Мб |
| 7. [Hands On] - Clean Missing Data with MICE.srt |
7.12Кб |
| 7. [Hands On] - Experiment Online Gradient.mp4 |
10.85Мб |
| 7. [Hands On] - Experiment Online Gradient.srt |
4.53Кб |
| 7. [Hands On] - Filter Based Selection - AzureML Experiment.mp4 |
6.37Мб |
| 7. [Hands On] - Filter Based Selection - AzureML Experiment.srt |
3.97Кб |
| 7.1 020 - add rows and columns using pandas.py |
532б |
| 7.1 140 - Dataset and Dataframe IO.py |
1.23Кб |
| 7.1 30 - Python While Loop.py |
340б |
| 7.1 MICE Loan Dataset.csv |
37.12Кб |
| 7. Add columns and rows.mp4 |
61.01Мб |
| 7. Add columns and rows.srt |
6.66Кб |
| 7. Create and Register a Datastore.mp4 |
89.86Мб |
| 7. Create and Register a Datastore.srt |
11.83Кб |
| 7. Decision Tree - What is Decision Tree.mp4 |
14.33Мб |
| 7. Decision Tree - What is Decision Tree.srt |
8.07Кб |
| 7. Dockers and Azure Container Registry.mp4 |
25.90Мб |
| 7. Dockers and Azure Container Registry.srt |
5.92Кб |
| 7. Getting Started with AzureML.html |
140б |
| 7. Pandas Dataframe and AzureML Dataset conversions.mp4 |
89.01Мб |
| 7. Pandas Dataframe and AzureML Dataset conversions.srt |
9.19Кб |
| 7. Recommendation System.html |
141б |
| 7. Understanding various aspects of data - Type, Variables, Category.mp4 |
13.61Мб |
| 7. Understanding various aspects of data - Type, Variables, Category.srt |
8.23Кб |
| 7. Update to Lecture Sequence..html |
134б |
| 7. While Loops in Python.mp4 |
26.40Мб |
| 7. While Loops in Python.srt |
5.49Кб |
| 70 |
257.83Кб |
| 71 |
312.45Кб |
| 72 |
240.51Кб |
| 73 |
304.81Кб |
| 74 |
97.23Кб |
| 75 |
353.91Кб |
| 76 |
732.73Кб |
| 77 |
788.62Кб |
| 78 |
842.12Кб |
| 79 |
496.00Кб |
| 8 |
955.61Кб |
| 8.1 030 - missing values part 1.py |
1.12Кб |
| 8.1 150 - File and Folder Upload.py |
1.31Кб |
| 8.1 40 - For loop.py |
318б |
| 8. Clean Missing Data.mp4 |
74.13Мб |
| 8. Clean Missing Data.srt |
6.56Кб |
| 8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4 |
13.31Мб |
| 8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.srt |
8.59Кб |
| 8. Create a Dataset.mp4 |
83.44Мб |
| 8. Create a Dataset.srt |
12.07Кб |
| 8. Data Processing.html |
140б |
| 8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp4 |
12.90Мб |
| 8. Decision Tree - Ensemble Learning - Bagging and Boosting.srt |
7.55Кб |
| 8. Decision Tree - What is Regression Tree.mp4 |
12.24Мб |
| 8. Decision Tree - What is Regression Tree.srt |
6.31Кб |
| 8. Fisher Based LDA - Intuition.mp4 |
24.08Мб |
| 8. Fisher Based LDA - Intuition.srt |
5.69Кб |
| 8. For Loop in Python.mp4 |
30.53Мб |
| 8. For Loop in Python.srt |
4.85Кб |
| 8. SMOTE - Create New Synthetic Observations.mp4 |
14.21Мб |
| 8. SMOTE - Create New Synthetic Observations.srt |
8.32Кб |
| 8. Upload local data to storage account via datastore.mp4 |
97.05Мб |
| 8. Upload local data to storage account via datastore.srt |
9.79Кб |
| 80 |
929.35Кб |
| 81 |
42.61Кб |
| 82 |
93.97Кб |
| 83 |
159.92Кб |
| 84 |
336.03Кб |
| 85 |
641.31Кб |
| 86 |
337.59Кб |
| 87 |
626.49Кб |
| 88 |
968.66Кб |
| 89 |
640.17Кб |
| 9 |
660.73Кб |
| 9. [Hands On] - Fisher Based LDA - Experiment.mp4 |
61.14Мб |
| 9. [Hands On] - Fisher Based LDA - Experiment.srt |
6.71Кб |
| 9. [Hands On] - SMOTE.mp4 |
15.54Мб |
| 9. [Hands On] - SMOTE.srt |
5.72Кб |
| 9.1 040 - Edit Metadata.py |
392б |
| 9.1 LoanSMOTE.csv |
6.20Кб |
| 9.1 Wine-Low-Medium-High.csv |
95.36Кб |
| 9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp4 |
12.09Мб |
| 9. Decision Tree - Parameters - Two Class Boosted Decision Tree.srt |
6.14Кб |
| 9. Decision Tree - What is Boosted Decision Tree Regression.mp4 |
4.32Мб |
| 9. Decision Tree - What is Boosted Decision Tree Regression.srt |
2.03Кб |
| 9. Edit Metadata of columns using Pandas.mp4 |
36.23Мб |
| 9. Edit Metadata of columns using Pandas.srt |
4.04Кб |
| 9. Explore the AzureML Dataset.mp4 |
26.89Мб |
| 9. Explore the AzureML Dataset.srt |
3.40Кб |
| 9. Problem Statement - Run a sample experiment and log values.mp4 |
12.91Мб |
| 9. Problem Statement - Run a sample experiment and log values.srt |
2.79Кб |
| 9. Python Lists.mp4 |
6.40Мб |
| 9. Python Lists.srt |
2.06Кб |
| 9. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4 |
19.04Мб |
| 9. Types of Machine Learning Models - Classification, Regression, Clustering etc.srt |
10.33Кб |
| 90 |
85.23Кб |
| 91 |
219.04Кб |
| 92 |
484.82Кб |
| 93 |
373.49Кб |
| 94 |
601.71Кб |
| 95 |
653.95Кб |
| 96 |
343.88Кб |
| 97 |
377.38Кб |
| 98 |
566.46Кб |
| 99 |
109.67Кб |
| TutsNode.com.txt |
63б |