Data Science And Machine Learning Bootcamp

In this I'm structuring Data Science and Machine Learning using python.
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Table of Contents

1. About The Repository
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. Contact

About The Repository

Topics Covered

• Data Cleaning and Pre-Processing
• Data Exploration and Visualisation
• Linear Regression
• Multivariable Regression
• Optimisation Algorithms and Gradient Descent
• Naive Bayes Classification
• Descriptive Statistics and Probability Theory
• Neural Networks and Deep Learning
• Model Evaluation and Analysis
• Serving a Tensorflow Model
• Image Processing using open-cv

Language and Libraries used

• Python
• Tensorflow
• Pandas
• Numpy
• Scikit Learn
• Keras
• Matplotlib
• Seaborn
• SciPy
• SymPy

Things that you can learn

• You will learn how to program using Python through practical projects
• Use data science algorithms to analyse data in real life projects such as spam classification and image recognition
• Build a portfolio of data science projects to apply for jobs in the industry
• Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

You should have python and pip installed on your machine.

• python
• pip
• Jupyter-Notebook

Installation

1. Clone the repo

   git clone https://github.com/your_username_/Project-Name.git

2. Open the terminal and install the libraries

   pip install LIBRARY_NAME

3. Then open Jupyter Notebook and run the file.

Usage

Use this space to show useful examples of how this repository can be used or improved. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

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 Work Branch (git checkout -b dev/your_name)
3. Commit your Changes (git commit -m 'Add some Files')
4. Push to the Branch (git push origin dev/your_name)
5. Open a Pull Request

Contact

• Syed Faique Ali - @faiqueali017 - [email protected]

• LinkedIn Profile: https://www.linkedin.com/in/faique-ali/