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The past can often be the key to predicting the future. Big data from historical sources is a valuable resource for identifying trends and building machine learning models that apply statistical patterns and predict future outcomes. The aim of this course is to introduce the Azure Machine Learning and explores techniques and to integrate predictive insights into big data processing workflows.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
Course Credit: Microsoft
Course Curriculum
| Introduction | |||
| Welcome | 00:01:00 | ||
| Preparing for the Labs | 00:05:00 | ||
| Lesson 1 | |||
| What Is Azure Machine Learning Studio? | 00:01:00 | ||
| Working with Workspaces | 00:05:00 | ||
| Datasets and Experiments | 00:09:00 | ||
| Notebooks | 00:06:00 | ||
| Managing Assets in Projects | 00:02:00 | ||
| The Cortana Intelligence Gallery | 00:03:00 | ||
| Data Sources in a Big Data Workflow | 00:01:00 | ||
| What Is Azure Machine Learning Studio? | 00:01:00 | ||
| Importing Files from Azure Storage | 00:07:00 | ||
| Importing Data from Azure SQL Database | 00:02:00 | ||
| Considerations for Big Data Sources | 00:01:00 | ||
| Lesson 2 | |||
| What is Machine Learning? | 00:03:00 | ||
| Regression | 00:04:00 | ||
| Training a Regression Model | 00:09:00 | ||
| Classification | 00:08:00 | ||
| Training a Classification Model | 00:07:00 | ||
| Clustering | 00:04:00 | ||
| Creating a K-Means Clustering Model | 00:04:00 | ||
| Recommenders | 00:02:00 | ||
| Creating a Recommender | 00:03:00 | ||
| Lesson 3 | |||
| Creating a Recommender | 00:02:00 | ||
| Creating a Predictive Experiment | 00:12:00 | ||
| Deploying a Web Service | 00:04:00 | ||
| Testing a Web Service | 00:03:00 | ||
| Considerations for Web Services | 00:01:00 | ||
| Consuming a Web Service | 00:06:00 | ||
| Using Web Service Parameters | 00:03:00 | ||
| Managing Web Services | 00:04:00 | ||
| Lesson 4 | |||
| Introduction to Azure Data Factory | 00:01:00 | ||
| Predicting in a Pipeline | 00:01:00 | ||
| Using the AzureMLBatchExecution Activity | 00:10:00 | ||
| Retraining in a Pipeline | 00:01:00 | ||
| Using the AzureMLUpdateResource Activity | 00:15:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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