waleed-javed/datascience

This Repo Contains Jupyter notebooks for DataScience Projects.

Data Science Projects

Data science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract
meaningful insights from data. In turn, these systems generate insights which can then be translated into tangible business values.
Explore the docs »

View Demo · Report Bug · Request Feature

About The Project

This Repo Contains Jupyter notebooks for DataScience Projects.

Built Via

• TensorFlow
• Google Collab
• Numpy
• Pandas
• Sk.Learn / Keras

Objectives

1. Explain how neural networks (deep and otherwise) compare to other machine learning models.
2. Determine when a deep neural network would be a good choice for a particular problem.
3. Demonstrate your understanding of the material through a final project uploaded to GitHub.

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• Google Collab / Jupyter Notebook
• Anaconda (If using locally)

Installation

1. Clone the repo

   git clone https://github.com/waleed-javed/DataScience.git

2. Open Anaconda CLI in the working directory and type

   jupyter notebook

3. Navigate to the NOTEBOOK_NAME.ipynb
4. Happy exploration my Scientist!

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/DataScience)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/DataScience)
5. Open a Pull Request

Contact

License

Distributed under the MIT License. See LICENSE for more information.