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| 1. [Hands On] - Data Input-Output - Upload Data.mp4 |
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| 1. [Hands On] - Data Input-Output - Upload Data.srt |
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| 1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp4 |
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| 1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.srt |
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| 1.1 Employee Dataset - Full.csv |
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| 1.1 Links for datasets.pdf |
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| 1. An Important Note..html |
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| 1. A note on Anaconda and Spyder..html |
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| 1. Azure ML Webservice - Prepare the experiment for webservice.mp4 |
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| 1. Azure ML Webservice - Prepare the experiment for webservice.srt |
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| 1. DP-100 Exam Curriculum.mp4 |
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| 1. Feature Selection - Section Introduction.mp4 |
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| 1. Introduction to AzureML SDK.mp4 |
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| 1. Logistic Regression - What is Logistic Regression.mp4 |
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| 1. To be Added.html |
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| 1. Understand the AzureMLService Architecture.mp4 |
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| 1. Way Forward.mp4 |
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| 1. What is a Recommendation System.mp4 |
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| 1. What is Cloud Computing.mp4 |
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| 1. What is Cluster Analysis.mp4 |
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| 1. What is Linear Regression.mp4 |
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| 1. What is Text Analytics or Natural Language Processing.mp4 |
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| 1. What You Will Learn in This Section.mp4 |
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| 10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4 |
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| 10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.srt |
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| 10.1 050 - Summarise the data.py |
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| 10.1 160 - Run a script and Log metrics.py |
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| 10.1 50 - Lists.py |
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| 10.1 Bank Telemarketing.csv |
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| 10. Basics of Machine Learning.html |
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| 10. Create Summary Statistics using describe.mp4 |
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| 10. Data Normalization - Scale and Reduce.mp4 |
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| 10. Python Lists - Operations Part 1.mp4 |
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| 10. Run a sample experiment using AzureML SDK - Part 1.mp4 |
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| 10. Understanding the AzureML Compute Resources.mp4 |
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| 11.1 060 - Outlier Detection and clipping.py |
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| 11. Clip Values - Remove Outliers using Constants.mp4 |
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| 11. Decision Forest - Parameters Explained.mp4 |
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| 12. Clip Values - Remove Outliers with Percentiles.mp4 |
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| 12. Multidimensional Lists in Python.mp4 |
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| 12. PCA - What is PCA and Curse of Dimensionality.mp4 |
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| 12. Run a script in Azureml environment - Part 1.mp4 |
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| 13. [Hands On] - Principal Component Analysis.mp4 |
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| 13.1 IRIS Dataset Link.txt |
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| 13. Convert and Save a delimited file using Pandas.mp4 |
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| 13. Create a Pipeline using AzureML Designer.mp4 |
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| 13. Run a script in Azureml environment - Part 2.mp4 |
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| 14. Data Normalization.mp4 |
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| 14. Data Normalization.srt |
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| 14. Join Data - Join Multiple Datasets based on common keys.mp4 |
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| 14. Python Tuples.mp4 |
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| 14. Run a script in Azureml environment - Part 3.mp4 |
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| 14. Submit the Designer Pipeline run.mp4 |
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| 14. SVM - What is Support Vector Machine.mp4 |
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| 15. [Hands On] - SVM - Adult Census Income Prediction.mp4 |
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| 15.1 EmpDeptJC.csv |
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| 15. Create an Inference Pipeline.mp4 |
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| 15. Label Encoding of String Categorical data.mp4 |
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| 15. Python Dictionary.mp4 |
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| 16. Classification Quiz.html |
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| 16. Deploy a real-time endpoint using Designer.mp4 |
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| 16. Python Dictionary Hands on Part 1.mp4 |
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| 16. Run a script in Azureml environment - Part 5.mp4 |
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| 17.1 100 - Create Dummy Variables.py |
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| 17. Create a batch inference pipeline using Designer.mp4 |
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| 17. Create a batch inference pipeline using Designer.srt |
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| 17. DP-100 Exam Coverage So far..mp4 |
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| 17. DP-100 Exam Coverage So far..srt |
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| 17. Hot Encoding using Pandas get_dummies.mp4 |
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| 18.1 110 - Split Data.py |
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| 18. Python Functions.mp4 |
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| 18. Python Functions.srt |
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| 18. Run a Batch Inference Pipeline from Designer.mp4 |
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| 18. Run a Batch Inference Pipeline from Designer.srt |
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| 18. Split The Data for training and testing.mp4 |
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| 18. Split The Data for training and testing.srt |
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| 18. Train and Run a Model Script in AzureML Part 1.mp4 |
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| 19.1 80 - Functions.py |
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| 19. Build Logistic Regression using Python - Part 1.mp4 |
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| 19. Build Logistic Regression using Python - Part 1.srt |
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| 19. Python Functions - Hands on.mp4 |
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| 19. Python Functions - Hands on.srt |
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| 19. Train and Run a Model Script in AzureML Part 2.mp4 |
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| 2. [Hands On] - Cluster Analysis Experiment 1.mp4 |
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| 2. [Hands On] - Cluster Analysis Experiment 1.srt |
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| 2. [Hands On] - Data Input-Output - Convert and Unpack.mp4 |
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| 2. [Hands On] - Data Input-Output - Convert and Unpack.srt |
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| 2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp4 |
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| 2. [Hands On] - Deploy Machine Learning Model As a Web Service.srt |
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| 2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4 |
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| 2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.srt |
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| 2.1 100 - Create Workspace and write config.py |
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| 2.1 Callcenter Data.csv |
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| 2.1 Employee Dataset - Full.zip |
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| 2.1 Loan Approval Prediction.csv |
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| 2. Bonus Lecture.html |
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| 2. Create AzureML Workspace using SDK.mp4 |
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| 2. Create AzureML Workspace using SDK.srt |
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| 2. Create the AzureML Workspace.mp4 |
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| 2. Data Preparation using Recommender Split.mp4 |
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| 2. Data Preparation using Recommender Split.srt |
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| 2. How to Summarize Data.mp4 |
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| 2. Hyperparameter Tuning.html |
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| 2. Install Anaconda.mp4 |
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| 2. Note on DP-100 Exam and New Studio.mp4 |
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| 2. Pearson Correlation Coefficient.mp4 |
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| 2. Pearson Correlation Coefficient.srt |
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| 2. Regression Analysis - Common Metrics.mp4 |
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| 2. Text Pre-Processing.mp4 |
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| 2. Text Pre-Processing.srt |
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| 2. What is Azure.mp4 |
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| 2. What is Azure.srt |
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| 2. What is Azure ML and high level architecture..mp4 |
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| 2. What is Azure ML and high level architecture..srt |
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| 2. What this section is about.mp4 |
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| 20.1 120 - Logistic regression.py |
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| 20.2 defaults.csv |
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| 20. Build Logistic Regression using Python - Part 2.mp4 |
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| 20. Build Logistic Regression using Python - Part 2.srt |
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| 20. Global Vs Local Variables in Python.mp4 |
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| 20. Global Vs Local Variables in Python.srt |
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| 20. Train and Run a Model Script in AzureML Part 3.mp4 |
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| 21. Types of Function Arguments.mp4 |
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| 21. Types of Function Arguments.srt |
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| 22.1 88 - Required Arguments.py |
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| 22. Function Arguments - Required Arguments.mp4 |
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| 22. Train and Run a Model Script in AzureML Part 5.mp4 |
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| 23.1 210 - Provision Compute Cluster.py |
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| 23.1 90 - Default Arguments.py |
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| 23. Function Arguments - Default Arguments.mp4 |
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| 23. Provisioning Compute Cluster using SDK.mp4 |
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| 24.1 92 - Keyword Arguments.py |
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| 24. Automate Model Training using AzureML SDK.mp4 |
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| 24. Function Arguments - Keyword Arguments.mp4 |
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| 25. Automate Model Training - Define Pipeline Steps.mp4 |
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| 25. Object Oriented Programming.mp4 |
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| 26.1 95 - class and objects.py |
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| 26. Automate Model Training - Define Run Configuration.mp4 |
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| 26. Define a Class and Create an Object.mp4 |
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| 27.1 220 - Pipeline Job.py |
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| 27. Automate Model Training - Define Build and Run.mp4 |
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| 27. Initialize the Class Attributes using __init__.mp4 |
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| 3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4 |
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| 3. [Hands On] - Data Input-Output - Import Data.mp4 |
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| 3. [Hands On] - Linear Regression model using OLS.mp4 |
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3.40MB |
| 3.13 Section 11 - Recommendation System.pdf |
3.10MB |
| 3.14 Section - Text Analytics.pdf |
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1.19MB |
| 3.1 Adult Dataset URL.txt |
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1.40MB |
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2.84MB |
| 3.6 All Data Files.zip |
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| 3.7 Section 01 - Basics of Machine Learning.pdf |
1.84MB |
| 3.8 Section 07 - Regression.pdf |
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| 3. Azure Basic Terms and Concepts.mp4 |
24.23MB |
| 3. Azure Basic Terms and Concepts.srt |
5.46KB |
| 3. Bag Of Words and N-Gram Models for Text features.mp4 |
49.96MB |
| 3. Bag Of Words and N-Gram Models for Text features.srt |
8.59KB |
| 3. Chi Square Test of Independence.mp4 |
8.28MB |
| 3. Chi Square Test of Independence.srt |
6.22KB |
| 3. Creating a Free Azure ML Account.mp4 |
23.70MB |
| 3. Creating a Free Azure ML Account.srt |
3.66KB |
| 3. Hello World and Know your environment.mp4 |
29.64MB |
| 3. Hello World and Know your environment.srt |
5.58KB |
| 3. Logistic Regression - Understand Parameters and Their Impact.mp4 |
19.54MB |
| 3. Logistic Regression - Understand Parameters and Their Impact.srt |
12.96KB |
| 3. Pandas - Import Data for Experiments.mp4 |
70.57MB |
| 3. Pandas - Import Data for Experiments.srt |
7.00KB |
| 3. The course slides as well as Data Files for all sections.html |
362B |
| 3. Verify the Workspace and Write the Workspace Config File.mp4 |
26.73MB |
| 3. Verify the Workspace and Write the Workspace Config File.srt |
3.29KB |
| 3. View and Manage Workspace Settings.mp4 |
44.43MB |
| 3. View and Manage Workspace Settings.srt |
5.57KB |
| 3. What is Matchbox Recommender and Train Matchbox Recommender.mp4 |
14.55MB |
| 3. What is Matchbox Recommender and Train Matchbox Recommender.srt |
8.28KB |
| 30 |
1016.64KB |
| 30.1 220 - Training Pipeline.py |
3.00KB |
| 30. Automate Model Training - Create Training Step.mp4 |
36.28MB |
| 30. Automate Model Training - Create Training Step.srt |
3.44KB |
| 31 |
277.92KB |
| 31. Run the pipeline and see the results.mp4 |
83.23MB |
| 31. Run the pipeline and see the results.srt |
9.97KB |
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374.89KB |
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267.87KB |
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448.77KB |
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257.89KB |
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288.26KB |
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299.29KB |
| 4 |
11B |
| 4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4 |
26.46MB |
| 4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.srt |
12.00KB |
| 4. [Hands On] - Linear Regression - R Squared.mp4 |
10.32MB |
| 4. [Hands On] - Linear Regression - R Squared.srt |
4.36KB |
| 4.1 004 - Logistic Regression - Understanding the results.xlsx |
23.96KB |
| 4.1 110 - Create Datastore.py |
918B |
| 4.1 Employee Dataset - TSV.txt |
1.85KB |
| 4.2 Employee Dataset - AR2.csv |
1.34KB |
| 4.3 Employee Dataset - AC1.csv |
1.62KB |
| 4.4 Employee Dataset - AC2.csv |
260B |
| 4.5 Employee Dataset - AR1.csv |
672B |
| 4. Azure ML Studio Overview and walk-through.mp4 |
12.17MB |
| 4. Azure ML Studio Overview and walk-through.srt |
5.18KB |
| 4. AzureML Web Service.html |
141B |
| 4. Azure Storage and Data Resource.mp4 |
34.37MB |
| 4. Azure Storage and Data Resource.srt |
9.70KB |
| 4. Clustering or Cluster Analysis.html |
141B |
| 4. Create and Register a Datastore using AzureML SDK.mp4 |
101.25MB |
| 4. Create and Register a Datastore using AzureML SDK.srt |
10.12KB |
| 4. Feature Hashing.mp4 |
75.17MB |
| 4. Feature Hashing.srt |
14.55KB |
| 4. How to Score the Matchbox Recommender.mp4 |
10.94MB |
| 4. How to Score the Matchbox Recommender.srt |
5.93KB |
| 4. Important Message About Udemy Reviews.mp4 |
4.69MB |
| 4. Important Message About Udemy Reviews.srt |
4.18KB |
| 4. Kendall Correlation Coefficient.mp4 |
6.70MB |
| 4. Kendall Correlation Coefficient.srt |
4.58KB |
| 4. Outliers Treatment - Clip Values.mp4 |
11.49MB |
| 4. Outliers Treatment - Clip Values.srt |
6.67KB |
| 4. Overview of New AzureML Studio.mp4 |
83.35MB |
| 4. Overview of New AzureML Studio.srt |
11.19KB |
| 4. Pandas - Import Data Part 2.mp4 |
50.42MB |
| 4. Pandas - Import Data Part 2.srt |
5.06KB |
| 4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4 |
29.41MB |
| 4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.srt |
13.64KB |
| 4. Variable Types in Python.mp4 |
54.74MB |
| 4. Variable Types in Python.srt |
8.98KB |
| 40 |
436.00KB |
| 41 |
115.33KB |
| 42 |
240.39KB |
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162.43KB |
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281.31KB |
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827.21KB |
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878.56KB |
| 47 |
1015.12KB |
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370.08KB |
| 49 |
2.78KB |
| 5 |
333.76KB |
| 5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4 |
38.91MB |
| 5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.srt |
18.71KB |
| 5. [Hands On] - Outliers Treatment - Clip Values.mp4 |
17.65MB |
| 5. [Hands On] - Outliers Treatment - Clip Values.srt |
7.46KB |
| 5. [Hands On] - Restaurant Recommendation Experiment.mp4 |
36.18MB |
| 5. [Hands On] - Restaurant Recommendation Experiment.srt |
13.13KB |
| 5.1 120 - Create and register a dataset.py |
1.17KB |
| 5.1 20 - Conditional Statements.py |
441B |
| 5.1 SQL Statement - Wine.txt |
141B |
| 5.2 Wine Quality Dataset.csv |
83.73KB |
| 5. Azure ML Experiment Workflow.mp4 |
13.22MB |
| 5. Azure ML Experiment Workflow.srt |
7.63KB |
| 5. Azure Storage hands on.mp4 |
97.55MB |
| 5. Azure Storage hands on.srt |
12.06KB |
| 5. Conditional Statements in Python.mp4 |
33.39MB |
| 5. Conditional Statements in Python.srt |
5.77KB |
| 5. Create and Register a Dataset using SDK.mp4 |
96.07MB |
| 5. Create and Register a Dataset using SDK.srt |
10.31KB |
| 5. DP-100 Exam Coverage So far..mp4 |
11.73MB |
| 5. DP-100 Exam Coverage So far..srt |
1.89KB |
| 5. Gradient Descent.mp4 |
27.66MB |
| 5. Gradient Descent.srt |
10.39KB |
| 5. Logistic Regression - Model Selection and Impact Analysis.mp4 |
13.77MB |
| 5. Logistic Regression - Model Selection and Impact Analysis.srt |
5.78KB |
| 5. Note for the next Hands On..html |
200B |
| 5. Select Columns using Pandas.mp4 |
80.72MB |
| 5. Select Columns using Pandas.srt |
7.50KB |
| 5. Spearman's Rank Correlation.mp4 |
6.37MB |
| 5. Spearman's Rank Correlation.srt |
4.10KB |
| 5. Why Machine Learning is the Future.mp4 |
68.71MB |
| 5. Why Machine Learning is the Future.srt |
10.46KB |
| 50 |
426.33KB |
| 51 |
266.11KB |
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402.83KB |
| 53 |
1019.65KB |
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623.57KB |
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589.44KB |
| 57 |
38.86KB |
| 58 |
407.82KB |
| 59 |
4.40KB |
| 6 |
460.23KB |
| 6. [Hands On] - Classify Customer Complaints using Text Analytics.mp4 |
90.99MB |
| 6. [Hands On] - Classify Customer Complaints using Text Analytics.srt |
10.98KB |
| 6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp4 |
13.19MB |
| 6. [Hands On] - Comparison Experiment for Correlation Coefficients.srt |
8.12KB |
| 6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4 |
19.66MB |
| 6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.srt |
8.68KB |
| 6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4 |
35.52MB |
| 6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.srt |
16.82KB |
| 6.1 130 - Access Workspace Datastore and Dataset.py |
1.52KB |
| 6.1 ml_studio_overview_v1.1.pdf |
2.25MB |
| 6.1 two-class complaints modified.txt |
47.39KB |
| 6.1 winequality-red.csv |
83.73KB |
| 6.2 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf |
404.10KB |
| 6. Access Workspace, Datastore and Datasets using SDK.mp4 |
110.73MB |
| 6. Access Workspace, Datastore and Datasets using SDK.srt |
10.43KB |
| 6. Azure ComputeVirtual Machines.mp4 |
21.75MB |
| 6. Azure ComputeVirtual Machines.srt |
4.34KB |
| 6. Azure ML Cheat Sheet for Model Selection.mp4 |
11.26MB |
| 6. Azure ML Cheat Sheet for Model Selection.srt |
6.64KB |
| 6. Clean Missing Data with MICE.mp4 |
13.06MB |
| 6. Clean Missing Data with MICE.srt |
7.00KB |
| 6. Linear Regression Online Gradient Descent.mp4 |
6.71MB |
| 6. Linear Regression Online Gradient Descent.srt |
2.22KB |
| 6. Python Loops explained..mp4 |
12.27MB |
| 6. Python Loops explained..srt |
2.68KB |
| 6. Select Columns By drop method.mp4 |
74.01MB |
| 6. Select Columns By drop method.srt |
7.52KB |
| 6. Understanding the Matchbox Recommendation Results.mp4 |
17.44MB |
| 6. Understanding the Matchbox Recommendation Results.srt |
8.27KB |
| 6. What is AzureML Datastore and Dataset.mp4 |
31.37MB |
| 6. What is AzureML Datastore and Dataset.srt |
7.13KB |
| 6. What is Machine Learning.mp4 |
18.49MB |
| 6. What is Machine Learning.srt |
11.13KB |
| 60 |
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576.50KB |
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630.92KB |
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| 66 |
947.94KB |
| 67 |
906.11KB |
| 68 |
157.97KB |
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193.80KB |
| 7 |
967.74KB |
| 7. [Hands On] - Clean Missing Data with MICE.mp4 |
15.92MB |
| 7. [Hands On] - Clean Missing Data with MICE.srt |
7.12KB |
| 7. [Hands On] - Experiment Online Gradient.mp4 |
10.85MB |
| 7. [Hands On] - Experiment Online Gradient.srt |
4.53KB |
| 7. [Hands On] - Filter Based Selection - AzureML Experiment.mp4 |
6.37MB |
| 7. [Hands On] - Filter Based Selection - AzureML Experiment.srt |
3.97KB |
| 7.1 020 - add rows and columns using pandas.py |
532B |
| 7.1 140 - Dataset and Dataframe IO.py |
1.23KB |
| 7.1 30 - Python While Loop.py |
340B |
| 7.1 MICE Loan Dataset.csv |
37.12KB |
| 7. Add columns and rows.mp4 |
61.01MB |
| 7. Add columns and rows.srt |
6.66KB |
| 7. Create and Register a Datastore.mp4 |
89.86MB |
| 7. Create and Register a Datastore.srt |
11.83KB |
| 7. Decision Tree - What is Decision Tree.mp4 |
14.33MB |
| 7. Decision Tree - What is Decision Tree.srt |
8.07KB |
| 7. Dockers and Azure Container Registry.mp4 |
25.90MB |
| 7. Dockers and Azure Container Registry.srt |
5.92KB |
| 7. Getting Started with AzureML.html |
140B |
| 7. Pandas Dataframe and AzureML Dataset conversions.mp4 |
89.01MB |
| 7. Pandas Dataframe and AzureML Dataset conversions.srt |
9.19KB |
| 7. Recommendation System.html |
141B |
| 7. Understanding various aspects of data - Type, Variables, Category.mp4 |
13.61MB |
| 7. Understanding various aspects of data - Type, Variables, Category.srt |
8.23KB |
| 7. Update to Lecture Sequence..html |
134B |
| 7. While Loops in Python.mp4 |
26.40MB |
| 7. While Loops in Python.srt |
5.49KB |
| 70 |
257.83KB |
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312.45KB |
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240.51KB |
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304.81KB |
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732.73KB |
| 77 |
788.62KB |
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842.12KB |
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496.00KB |
| 8 |
955.61KB |
| 8.1 030 - missing values part 1.py |
1.12KB |
| 8.1 150 - File and Folder Upload.py |
1.31KB |
| 8.1 40 - For loop.py |
318B |
| 8. Clean Missing Data.mp4 |
74.13MB |
| 8. Clean Missing Data.srt |
6.56KB |
| 8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4 |
13.31MB |
| 8. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.srt |
8.59KB |
| 8. Create a Dataset.mp4 |
83.44MB |
| 8. Create a Dataset.srt |
12.07KB |
| 8. Data Processing.html |
140B |
| 8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp4 |
12.90MB |
| 8. Decision Tree - Ensemble Learning - Bagging and Boosting.srt |
7.55KB |
| 8. Decision Tree - What is Regression Tree.mp4 |
12.24MB |
| 8. Decision Tree - What is Regression Tree.srt |
6.31KB |
| 8. Fisher Based LDA - Intuition.mp4 |
24.08MB |
| 8. Fisher Based LDA - Intuition.srt |
5.69KB |
| 8. For Loop in Python.mp4 |
30.53MB |
| 8. For Loop in Python.srt |
4.85KB |
| 8. SMOTE - Create New Synthetic Observations.mp4 |
14.21MB |
| 8. SMOTE - Create New Synthetic Observations.srt |
8.32KB |
| 8. Upload local data to storage account via datastore.mp4 |
97.05MB |
| 8. Upload local data to storage account via datastore.srt |
9.79KB |
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| 9 |
660.73KB |
| 9. [Hands On] - Fisher Based LDA - Experiment.mp4 |
61.14MB |
| 9. [Hands On] - Fisher Based LDA - Experiment.srt |
6.71KB |
| 9. [Hands On] - SMOTE.mp4 |
15.54MB |
| 9. [Hands On] - SMOTE.srt |
5.72KB |
| 9.1 040 - Edit Metadata.py |
392B |
| 9.1 LoanSMOTE.csv |
6.20KB |
| 9.1 Wine-Low-Medium-High.csv |
95.36KB |
| 9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp4 |
12.09MB |
| 9. Decision Tree - Parameters - Two Class Boosted Decision Tree.srt |
6.14KB |
| 9. Decision Tree - What is Boosted Decision Tree Regression.mp4 |
4.32MB |
| 9. Decision Tree - What is Boosted Decision Tree Regression.srt |
2.03KB |
| 9. Edit Metadata of columns using Pandas.mp4 |
36.23MB |
| 9. Edit Metadata of columns using Pandas.srt |
4.04KB |
| 9. Explore the AzureML Dataset.mp4 |
26.89MB |
| 9. Explore the AzureML Dataset.srt |
3.40KB |
| 9. Problem Statement - Run a sample experiment and log values.mp4 |
12.91MB |
| 9. Problem Statement - Run a sample experiment and log values.srt |
2.79KB |
| 9. Python Lists.mp4 |
6.40MB |
| 9. Python Lists.srt |
2.06KB |
| 9. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4 |
19.04MB |
| 9. Types of Machine Learning Models - Classification, Regression, Clustering etc.srt |
10.33KB |
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| TutsNode.com.txt |
63B |