2. Advanced Analytics

3. What is Machine Learning?

4. Common Examples Spam Email Filters Optical Character Recognition
Coverage Risks
Recommendations
Fraud Detection
Intrusion Detection
Predictive Maintenance

5. Types of Machine Learning

6. Machine Learning Process

7. Machine Learning Algorithms
Classification algorithms
classify data into different categories that can then be used to predict one or more discrete variables, based on the other attributes in the dataset.
Regression algorithms
predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
Clustering algorithms
determine natural groupings and patterns in datasets and are used to predict grouping classifications for a given variable.

8. Azure ML

9. Azure ML Studio

10. Demo: Creating an Azure Machine Learning experiment
Creating a workspace
Loading historical data
Visualizing the data
Splitting data into training and validation sets
Training the model

11. Evaluating Models Receiver Operator Characteristic (ROC) curves
Displays the fraction of true positives out of the total actual positives. The higher and further to the left, the more accurate the model is.
Precision/Recall curves
Precision represents the fraction of retrieved instances that are relevant.
Recall represents the fraction of relevant instances that are retrieved.
Lift curves This format is a variation on the ROC curve
Measures the fraction of true positives, in relation to the target response probability.

12. Comparing Models

13. Demo: Refining the Model
Score the model
Evaluate the model
Comparing models

14. Demo: Publish and Consume the Model
Creating the web service
Testing the web service
Calling RRS service from a client app
Batch execution

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