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kudoabhijeet / FoundationalML

Beginner's guide to Machine Learning. Notebooks, blogs, resources and tracks to help anyone get started.
MIT License
10 stars 3 forks source link
algorithms classification-model decision-trees fundamentals hacktoberfest jupyter-notebook knn-classification machine-learning matlplotlib naive-bayes-classifier numpy pandas python seaborn supervised-learning svm-classifier

readme

Foundational Machine Learning

Table of Contents

About The Project

Fundamentals of Machine Learning using Python.

Built With

Getting Started

This repository provides an overview of machine learning fundamentals. Topics covered include:

  1. Reviewing the types of problems that can be solved
  2. Understanding building blocks
  3. Learning the fundamentals of building models in machine learning
  4. Exploring key algorithms

Prerequisites

Installation

  1. Clone the repo 
    git clone https://github.com/kudoabhijeet/FoundationalML.git

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/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Contact

Abhijeet Prasad - @kudoabhijeet - abhi.prasad16@gmail.com

Project Link: https://github.com/kudoabhijeet/Machine-Learning