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DP-100 A-Z Machine Learning using Azure Machine Learning
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[TutsNode.com] - DP-100 A-Z Machine Learning using Azure Machine Learning (Size: 7.44 GB) (Files: 680)
[TutsNode.com] - DP-100 A-Z Machine Learning using Azure Machine Learning
15. DesignerClassic Studio Vs Pandas and Scikit-learn
14. Data Normalization.mp4
20.2 defaults.csv
20. Build Logistic Regression using Python - Part 2.mp4
20. Build Logistic Regression using Python - Part 2.srt
14. Data Normalization.srt
18. Split The Data for training and testing.srt
18. Split The Data for training and testing.mp4
15. Label Encoding of String Categorical data.srt
6. Select Columns By drop method.srt
5. Select Columns using Pandas.srt
10. Create Summary Statistics using describe.srt
12. Clip Values - Remove Outliers with Percentiles.srt
3. Pandas - Import Data for Experiments.srt
13. Convert and Save a delimited file using Pandas.srt
20.1 120 - Logistic regression.py
7. Add columns and rows.srt
8. Clean Missing Data.srt
11. Clip Values - Remove Outliers using Constants.srt
4. Pandas - Import Data Part 2.srt
19. Build Logistic Regression using Python - Part 1.srt
17. Hot Encoding using Pandas get_dummies.srt
9. Edit Metadata of columns using Pandas.srt
16. Why Hot encoding is required.srt
2. What this section is about.srt
8.1 030 - missing values part 1.py
3.1 010 - Pandas part 1.py
18.1 110 - Split Data.py
14.1 080 - Normalize the data.py
1. A note on Anaconda and Spyder..html
15.1 090 - Label encoding.py
11.1 060 - Outlier Detection and clipping.py
10.1 050 - Summarise the data.py
7.1 020 - add rows and columns using pandas.py
13.1 070 - Write to a delimited file.py
9.1 040 - Edit Metadata.py
17.1 100 - Create Dummy Variables.py
15. Label Encoding of String Categorical data.mp4
5. Select Columns using Pandas.mp4
8. Clean Missing Data.mp4
6. Select Columns By drop method.mp4
12. Clip Values - Remove Outliers with Percentiles.mp4
13. Convert and Save a delimited file using Pandas.mp4
10. Create Summary Statistics using describe.mp4
3. Pandas - Import Data for Experiments.mp4
7. Add columns and rows.mp4
4. Pandas - Import Data Part 2.mp4
19. Build Logistic Regression using Python - Part 1.mp4
11. Clip Values - Remove Outliers using Constants.mp4
17. Hot Encoding using Pandas get_dummies.mp4
9. Edit Metadata of columns using Pandas.mp4
16. Why Hot encoding is required.mp4
2. What this section is about.mp4
2. Getting Started with Azure ML
6.2 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf
1. What You Will Learn in This Section.srt
7. Getting Started with AzureML.html
5. Azure ML Experiment Workflow.srt
6. Azure ML Cheat Sheet for Model Selection.srt
4. Azure ML Studio Overview and walk-through.srt
2. What is Azure ML and high level architecture..srt
3. Creating a Free Azure ML Account.srt
3. Creating a Free Azure ML Account.mp4
5. Azure ML Experiment Workflow.mp4
4. Azure ML Studio Overview and walk-through.mp4
6. Azure ML Cheat Sheet for Model Selection.mp4
2. What is Azure ML and high level architecture..mp4
1. What You Will Learn in This Section.mp4
6.1 ml_studio_overview_v1.1.pdf
1. Basics of Machine Learning
1. What You Will Learn in This Section.srt
3. The course slides as well as Data Files for all sections.html
3.3 Section 06 - Deploy Webservice.pdf
3.6 All Data Files.zip
6. What is Machine Learning.srt
5. Why Machine Learning is the Future.srt
9. Types of Machine Learning Models - Classification, Regression, Clustering etc.srt
8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.srt
7. Understanding various aspects of data - Type, Variables, Category.srt
2. Note on DP-100 Exam and New Studio.srt
4. Important Message About Udemy Reviews.srt
10. Basics of Machine Learning.html
5. Why Machine Learning is the Future.mp4
2. Note on DP-100 Exam and New Studio.mp4
9. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4
6. What is Machine Learning.mp4
7. Understanding various aspects of data - Type, Variables, Category.mp4
8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4
1. What You Will Learn in This Section.mp4
4. Important Message About Udemy Reviews.mp4
3.12 Section 04 - Classification - 002 - Decision Tree.pdf
3.13 Section 11 - Recommendation System.pdf
3.10 Section 10 - Feature Selection.pdf
3.5 Section 09 - Data Processing.pdf
3.8 Section 07 - Regression.pdf
3.1 Section 02 - Getting Started with AzureML.pdf
3.14 Section - Text Analytics.pdf
3.7 Section 01 - Basics of Machine Learning.pdf
3.2 Section 08 - Clustering.pdf
3.4 Section 04 - Classification - 001 - Logistic Regression.pdf
3.15 Section 05 - Tune Hyperparameter.pdf
3.11 Section 04 - Classification - 003 - SVM.pdf
3.9 Section 03 - Data Pre-processing.pdf
12. Text Analytics and Natural Language Processing
6.1 two-class complaints modified.txt
2. Text Pre-Processing.srt
4. Feature Hashing.srt
6. [Hands On] - Classify Customer Complaints using Text Analytics.srt
3. Bag Of Words and N-Gram Models for Text features.srt
1. What is Text Analytics or Natural Language Processing.srt
5. Note for the next Hands On..html
6. [Hands On] - Classify Customer Complaints using Text Analytics.mp4
4. Feature Hashing.mp4
2. Text Pre-Processing.mp4
3. Bag Of Words and N-Gram Models for Text features.mp4
1. What is Text Analytics or Natural Language Processing.mp4
19. Python Crash Course
24. Function Arguments - Keyword Arguments.srt
26. Define a Class and Create an Object.srt
25. Object Oriented Programming.srt
18. Python Functions.srt
4. Variable Types in Python.srt
27. Initialize the Class Attributes using __init__.srt
20. Global Vs Local Variables in Python.srt
22. Function Arguments - Required Arguments.srt
28. Packages and Modules in Python.srt
23. Function Arguments - Default Arguments.srt
13. Slicing a multidimensional list.srt
5. Conditional Statements in Python.srt
2. Install Anaconda.srt
3. Hello World and Know your environment.srt
7. While Loops in Python.srt
8. For Loop in Python.srt
19. Python Functions - Hands on.srt
21. Types of Function Arguments.srt
16. Python Dictionary Hands on Part 1.srt
12. Multidimensional Lists in Python.srt
17. Python Dictionary Hands on Part 2.srt
10. Python Lists - Operations Part 1.srt
14. Python Tuples.srt
15. Python Dictionary.srt
6. Python Loops explained..srt
11. Python Lists - Operations Part 2.srt
9. Python Lists.srt
10.1 50 - Lists.py
16.1 70 - dictionary.py
26.1 95 - class and objects.py
23.1 90 - Default Arguments.py
19.1 80 - Functions.py
24.1 92 - Keyword Arguments.py
22.1 88 - Required Arguments.py
5.1 20 - Conditional Statements.py
1. An Important Note..html
7.1 30 - Python While Loop.py
8.1 40 - For loop.py
14.1 60 - tuples.py
26. Define a Class and Create an Object.mp4
27. Initialize the Class Attributes using __init__.mp4
4. Variable Types in Python.mp4
25. Object Oriented Programming.mp4
22. Function Arguments - Required Arguments.mp4
20. Global Vs Local Variables in Python.mp4
24. Function Arguments - Keyword Arguments.mp4
23. Function Arguments - Default Arguments.mp4
13. Slicing a multidimensional list.mp4
2. Install Anaconda.mp4
19. Python Functions - Hands on.mp4
5. Conditional Statements in Python.mp4
16. Python Dictionary Hands on Part 1.mp4
17. Python Dictionary Hands on Part 2.mp4
8. For Loop in Python.mp4
3. Hello World and Know your environment.mp4
12. Multidimensional Lists in Python.mp4
7. While Loops in Python.mp4
28. Packages and Modules in Python.mp4
10. Python Lists - Operations Part 1.mp4
11. Python Lists - Operations Part 2.mp4
18. Python Functions.mp4
21. Types of Function Arguments.mp4
15. Python Dictionary.mp4
14. Python Tuples.mp4
6. Python Loops explained..mp4
9. Python Lists.mp4
3. Data Processing
3.1 Adult Dataset URL.txt
4.4 Employee Dataset - AC2.csv
5.1 SQL Statement - Wine.txt
5.2 Wine Quality Dataset.csv
5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.srt
6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.srt
4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.srt
1.1 Employee Dataset - Full.csv
2.1 Employee Dataset - Full.zip
4.1 Employee Dataset - TSV.txt
4.2 Employee Dataset - AR2.csv
4.3 Employee Dataset - AC1.csv
4.5 Employee Dataset - AR1.csv
7. Update to Lecture Sequence..html
8. Data Processing.html
2. [Hands On] - Data Input-Output - Convert and Unpack.srt
1. [Hands On] - Data Input-Output - Upload Data.srt
3. [Hands On] - Data Input-Output - Import Data.srt
5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4
6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4
4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4
2. [Hands On] - Data Input-Output - Convert and Unpack.mp4
1. [Hands On] - Data Input-Output - Upload Data.mp4
3. [Hands On] - Data Input-Output - Import Data.mp4
10. Feature Selection - Select a subset of Variables or features with highest impact
9.1 Wine-Low-Medium-High.csv
6. [Hands On] - Comparison Experiment for Correlation Coefficients.srt
2. Pearson Correlation Coefficient.srt
1. Feature Selection - Section Introduction.srt
9. [Hands On] - Fisher Based LDA - Experiment.srt
3. Chi Square Test of Independence.srt
8. Fisher Based LDA - Intuition.srt
4. Kendall Correlation Coefficient.srt
5. Spearman's Rank Correlation.srt
7. [Hands On] - Filter Based Selection - AzureML Experiment.srt
9. [Hands On] - Fisher Based LDA - Experiment.mp4
2. Pearson Correlation Coefficient.mp4
8. Fisher Based LDA - Intuition.mp4
6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp4
3. Chi Square Test of Independence.mp4
1. Feature Selection - Section Introduction.mp4
4. Kendall Correlation Coefficient.mp4
5. Spearman's Rank Correlation.mp4
7. [Hands On] - Filter Based Selection - AzureML Experiment.mp4
8. Clustering
3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.srt
2. [Hands On] - Cluster Analysis Experiment 1.srt
1. What is Cluster Analysis.srt
2.1 Callcenter Data.csv
4. Clustering or Cluster Analysis.html
2. [Hands On] - Cluster Analysis Experiment 1.mp4
1. What is Cluster Analysis.mp4
3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4
16. Azure Machine Learning with AzureML SDK
6. Access Workspace, Datastore and Datasets using SDK.mp4
4. Create and Register a Datastore using AzureML SDK.srt
20. Train and Run a Model Script in AzureML Part 3.mp4
29. Automate Model Training - Create Dataprep Step.mp4
25. Automate Model Training - Define Pipeline Steps.srt
29. Automate Model Training - Create Dataprep Step.srt
28. Detour - Command Line Arguments.srt
23. Provisioning Compute Cluster using SDK.srt
6. Access Workspace, Datastore and Datasets using SDK.srt
5. Create and Register a Dataset using SDK.srt
11. Run a sample experiment using AzureML SDK - Part 2.srt
31. Run the pipeline and see the results.srt
8. Upload local data to storage account via datastore.srt
20. Train and Run a Model Script in AzureML Part 3.srt
19. Train and Run a Model Script in AzureML Part 2.srt
7. Pandas Dataframe and AzureML Dataset conversions.srt
22. Train and Run a Model Script in AzureML Part 5.srt
2. Create AzureML Workspace using SDK.srt
24. Automate Model Training using AzureML SDK.srt
14. Run a script in Azureml environment - Part 3.srt
10. Run a sample experiment using AzureML SDK - Part 1.srt
15. Run a script in Azureml environment - Part 4.srt
26. Automate Model Training - Define Run Configuration.srt
13. Run a script in Azureml environment - Part 2.srt
4. Create and Register a Datastore using AzureML SDK.mp4
16. Run a script in Azureml environment - Part 5.srt
18. Train and Run a Model Script in AzureML Part 1.srt
27. Automate Model Training - Define Build and Run.srt
21. Train and Run a Model Script in AzureML Part 4.srt
1. Introduction to AzureML SDK.srt
12. Run a script in Azureml environment - Part 1.srt
30. Automate Model Training - Create Training Step.srt
27.1 220 - Pipeline Job.py
3. Verify the Workspace and Write the Workspace Config File.srt
30.1 220 - Training Pipeline.py
9. Problem Statement - Run a sample experiment and log values.srt
17. DP-100 Exam Coverage So far..srt
29.1 220 - Dataprep Pipeline.py
10.1 160 - Run a script and Log metrics.py
6.1 130 - Access Workspace Datastore and Dataset.py
8.1 150 - File and Folder Upload.py
7.1 140 - Dataset and Dataframe IO.py
5.1 120 - Create and register a dataset.py
4.1 110 - Create Datastore.py
2.1 100 - Create Workspace and write config.py
23.1 210 - Provision Compute Cluster.py
8. Upload local data to storage account via datastore.mp4
5. Create and Register a Dataset using SDK.mp4
11. Run a sample experiment using AzureML SDK - Part 2.mp4
22. Train and Run a Model Script in AzureML Part 5.mp4
19. Train and Run a Model Script in AzureML Part 2.mp4
25. Automate Model Training - Define Pipeline Steps.mp4
7. Pandas Dataframe and AzureML Dataset conversions.mp4
23. Provisioning Compute Cluster using SDK.mp4
31. Run the pipeline and see the results.mp4
14. Run a script in Azureml environment - Part 3.mp4
28. Detour - Command Line Arguments.mp4
26. Automate Model Training - Define Run Configuration.mp4
13. Run a script in Azureml environment - Part 2.mp4
2. Create AzureML Workspace using SDK.mp4
10. Run a sample experiment using AzureML SDK - Part 1.mp4
15. Run a script in Azureml environment - Part 4.mp4
16. Run a script in Azureml environment - Part 5.mp4
21. Train and Run a Model Script in AzureML Part 4.mp4
27. Automate Model Training - Define Build and Run.mp4
18. Train and Run a Model Script in AzureML Part 1.mp4
30. Automate Model Training - Create Training Step.mp4
24. Automate Model Training using AzureML SDK.mp4
12. Run a script in Azureml environment - Part 1.mp4
1. Introduction to AzureML SDK.mp4
3. Verify the Workspace and Write the Workspace Config File.mp4
17. DP-100 Exam Coverage So far..mp4
9. Problem Statement - Run a sample experiment and log values.mp4
21. Thank You and Bonus Lecture
1.1 Links for datasets.pdf
2. Bonus Lecture.html
1. Way Forward.srt
1. Way Forward.mp4
4. Classification
6.1 winequality-red.csv
2.1 Loan Approval Prediction.csv
4.1 004 - Logistic Regression - Understanding the results.xlsx
2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.srt
12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.srt
4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.srt
3. Logistic Regression - Understand Parameters and Their Impact.srt
13.1 IRIS Dataset Link.txt
10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.srt
6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.srt
16. Classification Quiz.html
13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.srt
7. Decision Tree - What is Decision Tree.srt
8. Decision Tree - Ensemble Learning - Bagging and Boosting.srt
1. Logistic Regression - What is Logistic Regression.srt
9. Decision Tree - Parameters - Two Class Boosted Decision Tree.srt
5. Logistic Regression - Model Selection and Impact Analysis.srt
15. [Hands On] - SVM - Adult Census Income Prediction.srt
11. Decision Forest - Parameters Explained.srt
14. SVM - What is Support Vector Machine.srt
2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4
12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4
4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4
10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4
6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4
3. Logistic Regression - Understand Parameters and Their Impact.mp4
13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp4
14. SVM - What is Support Vector Machine.mp4
7. Decision Tree - What is Decision Tree.mp4
15. [Hands On] - SVM - Adult Census Income Prediction.mp4
5. Logistic Regression - Model Selection and Impact Analysis.mp4
8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp4
9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp4
1. Logistic Regression - What is Logistic Regression.mp4
11. Decision Forest - Parameters Explained.mp4
10.1 Bank Telemarketing.csv
9. Data Processing - Solving Data Processing Challenges
7.1 MICE Loan Dataset.csv
8. SMOTE - Create New Synthetic Observations.srt
5. [Hands On] - Outliers Treatment - Clip Values.srt
7. [Hands On] - Clean Missing Data with MICE.srt
6. Clean Missing Data with MICE.srt
4. Outliers Treatment - Clip Values.srt
12. PCA - What is PCA and Curse of Dimensionality.srt
2. How to Summarize Data.srt
14. Join Data - Join Multiple Datasets based on common keys.srt
9.1 LoanSMOTE.csv
11. [Hands On] - Data Normalization.srt
15. [Hands On] - Join Data - Experiment.srt
15.1 EmpDeptJC.csv
15.2 EmpSalaryJC.csv
9. [Hands On] - SMOTE.srt
13. [Hands On] - Principal Component Analysis.srt
3. [Hands On] - Summarize Data - Experiment.srt
1. Section Introduction.srt
10. Data Normalization - Scale and Reduce.srt
5. [Hands On] - Outliers Treatment - Clip Values.mp4
7. [Hands On] - Clean Missing Data with MICE.mp4
9. [Hands On] - SMOTE.mp4
15. [Hands On] - Join Data - Experiment.mp4
8. SMOTE - Create New Synthetic Observations.mp4
6. Clean Missing Data with MICE.mp4
2. How to Summarize Data.mp4
4. Outliers Treatment - Clip Values.mp4
12. PCA - What is PCA and Curse of Dimensionality.mp4
14. Join Data - Join Multiple Datasets based on common keys.mp4
3. [Hands On] - Summarize Data - Experiment.mp4
13. [Hands On] - Principal Component Analysis.mp4
11. [Hands On] - Data Normalization.mp4
1. Section Introduction.mp4
10. Data Normalization - Scale and Reduce.mp4
11. Recommendation System
1. What is a Recommendation System.srt
5. [Hands On] - Restaurant Recommendation Experiment.srt
2. Data Preparation using Recommender Split.srt
3. What is Matchbox Recommender and Train Matchbox Recommender.srt
6. Understanding the Matchbox Recommendation Results.srt
4. How to Score the Matchbox Recommender.srt
7. Recommendation System.html
5. [Hands On] - Restaurant Recommendation Experiment.mp4
1. What is a Recommendation System.mp4
6. Understanding the Matchbox Recommendation Results.mp4
2. Data Preparation using Recommender Split.mp4
3. What is Matchbox Recommender and Train Matchbox Recommender.mp4
4. How to Score the Matchbox Recommender.mp4
14. Azure Machine Learning with Studio Designer
15. Create an Inference Pipeline.srt
8. Create a Dataset.srt
7. Create and Register a Datastore.srt
14. Submit the Designer Pipeline run.srt
13. Create a Pipeline using AzureML Designer.srt
4. Overview of New AzureML Studio.srt
2. Create the AzureML Workspace.srt
16. Deploy a real-time endpoint using Designer.srt
1. Understand the AzureMLService Architecture.srt
17. Create a batch inference pipeline using Designer.srt
10. Understanding the AzureML Compute Resources.srt
6. What is AzureML Datastore and Dataset.srt
11. Create a Compute Cluster and Compute Instance.srt
12. What is an AzureML Pipeline.srt
3. View and Manage Workspace Settings.srt
18. Run a Batch Inference Pipeline from Designer.srt
9. Explore the AzureML Dataset.srt
5. DP-100 Exam Coverage So far..srt
13. Create a Pipeline using AzureML Designer.mp4
14. Submit the Designer Pipeline run.mp4
7. Create and Register a Datastore.mp4
8. Create a Dataset.mp4
4. Overview of New AzureML Studio.mp4
2. Create the AzureML Workspace.mp4
15. Create an Inference Pipeline.mp4
16. Deploy a real-time endpoint using Designer.mp4
17. Create a batch inference pipeline using Designer.mp4
11. Create a Compute Cluster and Compute Instance.mp4
3. View and Manage Workspace Settings.mp4
18. Run a Batch Inference Pipeline from Designer.mp4
10. Understanding the AzureML Compute Resources.mp4
1. Understand the AzureMLService Architecture.mp4
6. What is AzureML Datastore and Dataset.mp4
9. Explore the AzureML Dataset.mp4
12. What is an AzureML Pipeline.mp4
5. DP-100 Exam Coverage So far..mp4
20. Azure Fundamentals
5. Azure Storage hands on.srt
4. Azure Storage and Data Resource.srt
1. What is Cloud Computing.srt
7. Dockers and Azure Container Registry.srt
3. Azure Basic Terms and Concepts.srt
6. Azure ComputeVirtual Machines.srt
2. What is Azure.srt
5. Azure Storage hands on.mp4
1. What is Cloud Computing.mp4
4. Azure Storage and Data Resource.mp4
7. Dockers and Azure Container Registry.mp4
3. Azure Basic Terms and Concepts.mp4
6. Azure ComputeVirtual Machines.mp4
2. What is Azure.mp4
7. Regression Analysis
3. [Hands On] - Linear Regression model using OLS.srt
5. Gradient Descent.srt
6. Linear Regression Online Gradient Descent.srt
9. Decision Tree - What is Boosted Decision Tree Regression.srt
11. Regression Analysis.html
10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.srt
2. Regression Analysis - Common Metrics.srt
8. Decision Tree - What is Regression Tree.srt
1. What is Linear Regression.srt
7. [Hands On] - Experiment Online Gradient.srt
4. [Hands On] - Linear Regression - R Squared.srt
3. [Hands On] - Linear Regression model using OLS.mp4
5. Gradient Descent.mp4
10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp4
1. What is Linear Regression.mp4
2. Regression Analysis - Common Metrics.mp4
8. Decision Tree - What is Regression Tree.mp4
7. [Hands On] - Experiment Online Gradient.mp4
4. [Hands On] - Linear Regression - R Squared.mp4
6. Linear Regression Online Gradient Descent.mp4
9. Decision Tree - What is Boosted Decision Tree Regression.mp4
5. Hyperparameter Tuning
1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.srt
2. Hyperparameter Tuning.html
1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp4
13. ------- DP - 100 Certification Exam ---------
1. DP-100 Exam Curriculum.srt
1. DP-100 Exam Curriculum.mp4
6. Deploy Webservice
1. Azure ML Webservice - Prepare the experiment for webservice.srt
4. AzureML Web Service.html
3. [Hands On] - Use the Web Service - Example of Excel.srt
2. [Hands On] - Deploy Machine Learning Model As a Web Service.srt
3. [Hands On] - Use the Web Service - Example of Excel.mp4
2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp4
1. Azure ML Webservice - Prepare the experiment for webservice.mp4
17. Azure AutoML
1. To be Added.html
18. Azure Hyperdrive
1. To be Added.html
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