AWS-Machine-Learning-Scholarship-Program

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AWS-Machine-Learning-Scholarship-Program

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This project focuses on best practices of object-oriented programming, AWS AI Services, AWS DeepLens, AWS DeepRacer, and AWS DeepComposer.

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# AWS Machine Learning Scholarship Program

This project focuses on best practices of object-oriented programming, AWS AI Services, AWS DeepLens, AWS DeepRacer, and AWS DeepComposer.

## Table of Contents

1. [aws machine learning scholarship quiz answers](#se1)
2. [Software Engineering Practices Pt II](#se2)
3. [Introduction to Object-Oriented Programming](#ioop)
4. [Machine Learning with AWS DeepComposer](#mlaws)
 
 
## Installation

### :fire: Anaconda

The Anaconda distribution is free to install and can be downloaded using:

[https://www.anaconda.com/download/](https://www.anaconda.com/download/)

Based on your operating system, installation instructions are available online:

- [Installing on macOS](https://docs.anaconda.com/anaconda/install/mac-os/)
- [Installing on Linux](https://docs.anaconda.com/anaconda/install/linux/)
- [Installing on Windows](https://docs.anaconda.com/anaconda/install/windows/)

### :snake: Python 3

Python 3 is required to run the files within this repository.

## Run
In a terminal or command window, navigate to the top-level project directory `AWS-Machine-Learning-Scholarship-Program/` (that contains this README) and run the following command:

```bash
jupyter notebook jupyter_notebook.ipynb
```

or
```bash
jupyter notebook jupyter_notebook.ipynb
```

on any Jupyter Notebook.
This will open the iPython Notebook software and project file in your browser.

## Additional Resources

[PEP 257 - Docstring Conventions](https://www.python.org/dev/peps/pep-0257/)

[NumPy Docstring Guide](https://numpydoc.readthedocs.io/en/latest/format.html)

[How do I create some kind of table of content in GitHub wiki?](https://stackoverflow.com/questions/18244417/how-do-i-create-some-kind-of-table-of-content-in-github-wiki)

## Examples on documentation

[Udacity's course: Writing READMEs](https://classroom.udacity.com/courses/ud777)

[Bootstrap](https://github.com/twbs/bootstrap)

[Scikit-learn](https://github.com/scikit-learn/scikit-learn)

[Stack Overflow Blog](https://github.com/jjrunner/stackoverflow)

## Version Control

[A successful Git branching model](https://nvie.com/posts/a-successful-git-branching-model/)

[How to version control your production machine learning models](https://algorithmia.com/blog/how-to-version-control-your-production-machine-learning-models)

[Versioning Data Science Solutions](https://medium.com/hashmapinc/versioning-data-science-solutions-28c8e7d47cd1)

## Test Driven Development for Data Science

[Data Science and Test Driven Development](https://www.linkedin.com/pulse/data-science-test-driven-development-sam-savage/)

[Test-Driven Development for Data Science](http://engineering.pivotal.io/post/test-driven-development-for-data-science/)

[Test Driven Development is essential for good data science](https://medium.com/uk-hydrographic-office/test-driven-development-is-essential-for-good-data-science-heres-why-db7975a03a44)

[Testing Your Code](https://docs.python-guide.org/writing/tests/)

## Code Reviews

[Code Review](https://github.com/lyst/MakingLyst/tree/master/code-reviews)

[Code Review Best Practices](https://www.kevinlondon.com/2015/05/05/code-review-best-practices.html)

## OOP topics

[Python - public, private and protected Access Modifiers](https://www.tutorialsteacher.com/python/private-and-protected-access-modifiers-in-python)

## Advanced OOP Topics

[Python's Instance, Class, and Static Methods Demystified](https://realpython.com/instance-class-and-static-methods-demystified/)

[Class and Instance Attributes](https://www.python-course.eu/python3_class_and_instance_attributes.php)

[Mixins for Fun and Profit](https://easyaspython.com/mixins-for-fun-and-profit-cb9962760556)

[Primer on Python Decorators](https://realpython.com/primer-on-python-decorators/)

## Explore Generative AI Further

[Create compositions using sample models in music studio](https://console.aws.amazon.com/deepcomposer/home?region=us-east-1#musicStudio)

[Inspect the training of existing sample models](https://console.aws.amazon.com/deepcomposer/home?region=us-east-1#modelList)

[Train your own model within the AWS DeepComposer console](https://console.aws.amazon.com/deepcomposer/home?region=us-east-1#trainModel)

[Build your own GAN model](https://github.com/aws-samples/aws-deepcomposer-samples)

## License

This project uses the [MIT](https://choosealicense.com/licenses/mit/) License.

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