Predictive Analytics: Detecting Kidney Disease Using AI
Predictive Analytics 2022
| Intermediate
- 17 videos | 1h 47m 39s
- Includes Assessment
- Earns a Badge
Nowadays, diseases such as Alzheimer's, heart disease, and diabetes are becoming ever more prevalent across the world. Use this course to get hands-on experience building a pipeline to diagnose chronic kidney disease using Azure Machine Learning designer. Explore the different features of Azure Machine Learning, its interface, and how components and resources come together to build a pipeline. Next, learn how to build a pipeline to create a dataset, implement various data cleaning tasks, and work with the cleaned dataset to build a logistic regression model to detect kidney disease. Finally, examine how models can be trained and evaluated for performance and deploy your pipeline. Upon completion, you'll be able to build and deploy a disease diagnosis Azure Machine Learning pipeline.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courserecall the features of an Azure Machine Learning workspacecreate a dataset from a CSV file containing kidney disease datastudy statistics for different fields in a dataset and generate a profilecreate a simple pipeline that will accept a dataset as inputmark specific features in a dataset as containing categorical valuesapply data cleaning to numeric fields in a datasethandle missing values in categorical fields in a datasetremove duplicate rows from a dataset
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group numeric attributes into bins so that they can be treated as categorical fieldsregister cleaned and processed data as a datasetset up an ML pipeline for disease diagnosissplit a dataset into train and test setstrain a model and view its performance metricscreate and use a real-time inferencing pipelinedeploy a kidney disease model to an Azure container and consume itsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 57sUP NEXT
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2.Provisioning an Azure Machine Learning Workspace9m 55s
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3.Creating a Dataset for an ML Pipeline8m 50s
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4.Studying the Dataset and Generating a Profile8m 3s
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5.Building and Running a Pipeline8m 33s
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6.Marking Fields as Categorical11m 6s
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7.Cleaning Numeric Data5m 24s
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8.Handling Missing Categorical Values5m 26s
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9.Eliminating Duplicate Rows5m 6s
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10.Grouping Numeric Fields into Bins6m 43s
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11.Registering a Cleaned Dataset2m 5s
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12.Building an ML Training Pipeline5m 58s
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13.Splitting a Dataset into Train and Test Sets3m 54s
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14.Training and Evaluating a Model9m 9s
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15.Building a Real-Time Inferencing Pipeline6m 5s
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16.Deploying a Model for Inferencing6m 35s
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17.Course Summary2m 50s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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