merged top level README
This commit is contained in:
John Mark Agosta
Родитель
e59d61e69b
Коммит
429fb233df
145
README.md
145
README.md
|
|
@ -1,104 +1,41 @@
|
|||
<<<<<<< HEAD
|
||||
---
|
||||
page_type: workshop
|
||||
languages:
|
||||
- python, sql, r
|
||||
products:
|
||||
- Personalizer Service, App Services
|
||||
description: "This hands-on, self-service lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions."
|
||||
---
|
||||
|
||||
# Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions
|
||||
|
||||
This hands-on, self-service lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions.
|
||||
Our goal is to empower decision makers to make more effective use of machine learning results and be better able to evaluate opportunities to apply ML in their industries.
|
||||
You will get a brief overview of what it means to discover generalizable patterns in data, and learn basic principles of how to apply probabilistic results, including optimizing the business value of applying machine learning classifiers based on their sensitivity and specificity.
|
||||
Finally, you will learn how ML and advanced analytics can help to guide (but not replace) the process of experimentally testing the effects of incremental changes to products and processes.
|
||||
|
||||
This is a non-programming workshop for business leaders and other people involved in managing products and making decisions based on data. The hands-on exercises require a web browser and Microsoft Excel.
|
||||
|
||||
This contents is available in the repository [datascience4managers](https://github.com/microsoft/datascience4managers).
|
||||
|
||||
## Contents
|
||||
|
||||
[Part 1](Part_1) - Learn how machine learning (ML) differs from traditional software engineering
|
||||
|
||||
[Part 2](Part_2) - See how ML fits in the context of making better business decisions
|
||||
|
||||
[Part 3](Part_3) - Understand why causal relationships matter in data analysis, and why we still need to do experiments
|
||||
|
||||
## Contributing
|
||||
|
||||
This project welcomes contributions and suggestions. Most contributions require you to agree to a
|
||||
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
|
||||
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
|
||||
|
||||
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
|
||||
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
|
||||
provided by the bot. You will only need to do this once across all repos using our CLA.
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
||||
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
|
||||
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
|
||||
=======
|
||||
---
|
||||
page_type: workshop
|
||||
languages:
|
||||
- python, sql, r
|
||||
products:
|
||||
- Personalizer Service, App Services
|
||||
description: "Add 150 character max description"
|
||||
---
|
||||
|
||||
# Data Science for Managers
|
||||
|
||||
This lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions. Our goal is to empower decision makers to make more effective use of machine learning results and be better able to evaluate opportunities to apply ML in their industries. You will get a brief overview of what it means to discover generalizable patterns in data, and learn basic principles of how to apply probabilistic results, including optimizing the business value of applying machine learning classifiers based on their sensitivity and specificity. Finally, you will learn how ML and advanced analytics can help to guide (but not replace) the process of experimentally testing the effects of incremental changes to products and processes.
|
||||
|
||||
This is a non-programming workshop for business leaders and other people involved in managing products and making decisions based on data. The hands-on exercises require a web browser and Microsoft Excel.
|
||||
|
||||
|
||||
|
||||
|
||||
## Contents
|
||||
|
||||
Outline the file contents of the repository. It helps users navigate the codebase, build configuration and any related assets.
|
||||
|
||||
| File/folder | Description |
|
||||
|-------------------|--------------------------------------------|
|
||||
| `src` | Sample source code. |
|
||||
| `.gitignore` | Define what to ignore at commit time. |
|
||||
| `CHANGELOG.md` | List of changes to the sample. |
|
||||
| `CONTRIBUTING.md` | Guidelines for contributing to the sample. |
|
||||
| `README.md` | This README file. |
|
||||
| `LICENSE` | The license for the sample. |
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Outline the required components and tools that a user might need to have on their machine in order to run the sample. This can be anything from frameworks, SDKs, OS versions or IDE releases.
|
||||
|
||||
## Setup
|
||||
|
||||
Explain how to prepare the sample once the user clones or downloads the repository. The section should outline every step necessary to install dependencies and set up any settings (for example, API keys and output folders).
|
||||
|
||||
## Runnning the sample
|
||||
|
||||
Outline step-by-step instructions to execute the sample and see its output. Include steps for executing the sample from the IDE, starting specific services in the Azure portal or anything related to the overall launch of the code.
|
||||
|
||||
## Key concepts
|
||||
|
||||
Provide users with more context on the tools and services used in the sample. Explain some of the code that is being used and how services interact with each other.
|
||||
|
||||
## Contributing
|
||||
|
||||
This project welcomes contributions and suggestions. Most contributions require you to agree to a
|
||||
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
|
||||
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
|
||||
|
||||
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
|
||||
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
|
||||
provided by the bot. You will only need to do this once across all repos using our CLA.
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
||||
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
|
||||
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
|
||||
>>>>>>> spring2020MLADS
|
||||
---
|
||||
page_type: workshop
|
||||
languages:
|
||||
- python, sql, r
|
||||
products:
|
||||
- Personalizer Service, App Services
|
||||
description: "This hands-on, self-service lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions."
|
||||
---
|
||||
|
||||
# Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions
|
||||
|
||||
This hands-on, self-service lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions.
|
||||
Our goal is to empower decision makers to make more effective use of machine learning results and be better able to evaluate opportunities to apply ML in their industries.
|
||||
You will get a brief overview of what it means to discover generalizable patterns in data, and learn basic principles of how to apply probabilistic results, including optimizing the business value of applying machine learning classifiers based on their sensitivity and specificity.
|
||||
Finally, you will learn how ML and advanced analytics can help to guide (but not replace) the process of experimentally testing the effects of incremental changes to products and processes.
|
||||
|
||||
This is a non-programming workshop for business leaders and other people involved in managing products and making decisions based on data. The hands-on exercises require a web browser and Microsoft Excel.
|
||||
|
||||
This contents is available in the repository [datascience4managers](https://github.com/microsoft/datascience4managers).
|
||||
|
||||
## Contents
|
||||
|
||||
[Part 1](Part_1) - Learn how machine learning (ML) differs from traditional software engineering
|
||||
|
||||
[Part 2](Part_2) - See how ML fits in the context of making better business decisions
|
||||
|
||||
[Part 3](Part_3) - Understand why causal relationships matter in data analysis, and why we still need to do experiments
|
||||
|
||||
## Contributing
|
||||
|
||||
This project welcomes contributions and suggestions. Most contributions require you to agree to a
|
||||
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
|
||||
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
|
||||
|
||||
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
|
||||
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
|
||||
provided by the bot. You will only need to do this once across all repos using our CLA.
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
||||
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
|
||||
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче