Table of Contents
Machine Learning Guide is an open source project which I made as collection of different Machine Learning Models collection to act as boiler plate template for professionals or act as theoratical and prcatical guide for newbies.
This project contains most of machine learning models and still growing in ipynb notebook format. Each notebook contains theory relevant to that model, implementation and explaination of each code cell.
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- model_name.ipynb - General format of model file.
- Datasets - All datasets in csv, tsv, txt format
- Image Dataset - For CNN this repo contains set of 9000 images
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.
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.
$ git clone https://github.com/AbhijeetSrivastav/Machine-Learning-Guide.git$ conda install -r requirements.txt$ cd Project Directory$ jupyter notebook "model_name.ipynb"
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All major famous Machine Learning and Deep Learning Libraries have been used in the projcet.
You can install them either by installing them indivdually as per your requirment using your favourite package mangager.
$ pip install package$ conda install packageOr you can install all the required packages at once by using this requirements.txt file.
$ pip install -r requirements.txt
This Machine Learning Guide project is made as collection of several Jupyter Noetbooks that can be used as guide by newbie to get theoratical and practical test of machine learning models or by professional as bolier plate for their project.
Models are present in different models with a dataset to test the model and learn from the outputs. Notebooks contain theory related to model, code implementataion and expaination of implementation.
The Machine Learning Guide Project is a open source project. I a committed to a fully transparent development process and highly appreciate any contributions. Whether you are helping me fixing bugs, proposing new feature, improving our documentation or spreading the word - we would love to have you as part of this project.
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.
- Fork the Project
- Create your Model Branch (
git checkout -b model/AmazingModel) - Commit your Changes (
git commit -m 'Add some AmazingModel') - Push to the Branch (
git push origin model/AmazingModel) - Open a Pull Request
I open-source almost everything I can, and I try to reply to everyone needing help using these projects. Obviously, this takes time. You can use this service for free.
However, if you are using this project and are happy with it or just want to encourage me to continue creating stuff, there are few ways you can do it :-
- Giving proper credit when you use Machine Learning Guide, linking back to it :D
- Starring and sharing the project 🚀
You can make one-time donations via PayPal. I'll probably buy a
coffeetea. 🍵
Thanks! ❤️
The MIT License (MIT)
Copyright (c) 2021 Abhijeet Srivastav
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software
is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or
substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Abhijeet Srivastav
