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Название DP-100 A-Z Machine Learning using Azure Machine Learning
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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
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1. DP-100 Exam Curriculum.mp4 61.19Мб
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1. Feature Selection - Section Introduction.mp4 7.73Мб
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1. Introduction to AzureML SDK.mp4 26.86Мб
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1. Logistic Regression - What is Logistic Regression.mp4 11.48Мб
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1. Section Introduction.mp4 5.41Мб
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1. To be Added.html 103б
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1. Understand the AzureMLService Architecture.mp4 34.91Мб
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1. Way Forward.mp4 57.00Мб
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1. What is a Recommendation System.mp4 34.96Мб
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1. What is Cloud Computing.mp4 34.84Мб
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1. What is Cluster Analysis.mp4 22.38Мб
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1. What is Linear Regression.mp4 14.03Мб
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1. What is Text Analytics or Natural Language Processing.mp4 40.70Мб
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1. What You Will Learn in This Section.mp4 12.39Мб
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10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp4 17.28Мб
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10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4 25.16Мб
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10. Data Normalization - Scale and Reduce.mp4 5.33Мб
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11. Decision Forest - Parameters Explained.mp4 5.79Мб
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11. Regression Analysis.html 141б
11. Run a sample experiment using AzureML SDK - Part 2.mp4 94.15Мб
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12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4 35.09Мб
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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
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12. PCA - What is PCA and Curse of Dimensionality.mp4 10.73Мб
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12. Run a script in Azureml environment - Part 1.mp4 27.63Мб
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13. [Hands On] - Principal Component Analysis.mp4 7.40Мб
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15. [Hands On] - SVM - Adult Census Income Prediction.mp4 13.83Мб
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15.1 EmpDeptJC.csv 108б
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15. Label Encoding of String Categorical data.mp4 84.99Мб
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16. Deploy a real-time endpoint using Designer.mp4 66.57Мб
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16. Python Dictionary Hands on Part 1.mp4 32.05Мб
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17. Create a batch inference pipeline using Designer.mp4 61.84Мб
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17. DP-100 Exam Coverage So far..mp4 13.91Мб
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17. Hot Encoding using Pandas get_dummies.mp4 37.65Мб
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18. Train and Run a Model Script in AzureML Part 1.mp4 45.38Мб
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19.1 80 - Functions.py 550б
19. Build Logistic Regression using Python - Part 1.mp4 44.07Мб
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19. Python Functions - Hands on.mp4 33.67Мб
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19. Train and Run a Model Script in AzureML Part 2.mp4 90.53Мб
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2. [Hands On] - Data Input-Output - Convert and Unpack.mp4 22.08Мб
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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
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2. Create the AzureML Workspace.mp4 76.36Мб
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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
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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
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2. What is Azure.mp4 18.10Мб
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2. What is Azure ML and high level architecture..mp4 7.03Мб
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2. What this section is about.mp4 10.76Мб
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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
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21. Types of Function Arguments.mp4 15.27Мб
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22.1 88 - Required Arguments.py 520б
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23.1 210 - Provision Compute Cluster.py 813б
23.1 90 - Default Arguments.py 571б
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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
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25. Automate Model Training - Define Pipeline Steps.mp4 89.32Мб
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25. Object Oriented Programming.mp4 53.00Мб
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28. Detour - Command Line Arguments.mp4 72.73Мб
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28. Packages and Modules in Python.mp4 26.35Мб
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29. Automate Model Training - Create Dataprep Step.mp4 109.63Мб
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3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4 18.36Мб
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3. [Hands On] - Data Input-Output - Import Data.mp4 13.12Мб
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3. [Hands On] - Linear Regression model using OLS.mp4 91.04Мб
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3. [Hands On] - Summarize Data - Experiment.mp4 8.14Мб
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3. [Hands On] - Use the Web Service - Example of Excel.mp4 16.58Мб
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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
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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
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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
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3. Creating a Free Azure ML Account.mp4 23.70Мб
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3. Hello World and Know your environment.mp4 29.64Мб
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3. Logistic Regression - Understand Parameters and Their Impact.mp4 19.54Мб
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3. Pandas - Import Data for Experiments.mp4 70.57Мб
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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
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3. What is Matchbox Recommender and Train Matchbox Recommender.mp4 14.55Мб
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4. AzureML Web Service.html 141б
4. Azure Storage and Data Resource.mp4 34.37Мб
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4. Create and Register a Datastore using AzureML SDK.mp4 101.25Мб
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6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4 19.66Мб
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7. Getting Started with AzureML.html 140б
7. Pandas Dataframe and AzureML Dataset conversions.mp4 89.01Мб
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9. Problem Statement - Run a sample experiment and log values.mp4 12.91Мб
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