Creating your first data science experiment in azure machine learning studio
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Delivered for #aOSMumbai as a level 101 session on getting started with Azure Machine learning studio. In this session we showed how to predict the price of a car.
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Creating your first data science experiment in azure machine learning studio
- 2. Agenda • How to Get Started? • What is Machine Learning Studio? • Overview • Capabilities • Interface • Components of an Experiment • Scenario • DEMO
- 3. How to get started?
- 4. What is Machine Learning Studio? Interactive Visual Workspace Drag and Drop Tool Predictive Analytics Solutions Web Service Consumable End-Point
- 7. Machine Learning Studio Interface
- 8. Components of an Experiment • The experiment has at least one dataset and one module • Datasets may be connected only to modules • Modules may be connected to either datasets or other modules • All input ports for modules must have some connection to the data flow • All required parameters for each module must be set
- 9. Scenario Predict the price of an automobile based on different variables such as make and technical specifications
- 10. Demo • Create a model • Get Data • Prepare the data • Define features • Train the model • Choose and apply a learning algorithm • Score and test the model • Predict new automobile prices
- 11. Contact Details Twitter: @jasjitchopra LinkedIn: https://www.linkedin.com/in/jasjitchopra/ SlideShare: https://www.slideshare.net/jasjitchopra
- 12. Thanks to our sponsor!
- 14. How to pick the right algorithm
- 15. How to pick the right algorithm
- 16. How to pick the right algorithm
- 17. How to pick the right algorithm
- 18. How to pick the right algorithm
Editor's Notes
- If you've never used Azure Machine Learning Studio before, this session is for you. This session is not for Advanced users. We will not be covering algorithms here.
- Quick Evaluation: https://studio.azureml.net/Home/Anonymous Free Workspace: https://studio.azureml.net/Home Standard Workspace: https://azure.microsoft.com/en-us/documentation/articles/machine-learning-create-workspace
- http://download.microsoft.com/download/C/4/6/C4606116-522F-428A-BE04-B6D3213E9E52/ml_studio_overview_v1.1.pdf
- PROJECTS - Collections of experiments, datasets, notebooks, and other resources representing a single project EXPERIMENTS - Experiments that you have created and run or saved as drafts WEB SERVICES - Web services that you have deployed from your experiments NOTEBOOKS - Jupyter notebooks that you have created DATASETS - Datasets that you have uploaded into Studio TRAINED MODELS - Models that you have trained in experiments and saved in Studio SETTINGS - A collection of settings that you can use to configure your account and resources.