Add first initial readme for the crate

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "rustic_ml"
-version = "0.0.2"
+version = "0.0.3"
 authors = ["Kjetil Indrehus <kjetilindrehus@gmail.com>"]
 edition = "2021"
 license = "MIT"

README.md

@@ -1,5 +1,92 @@
-# rustic_ml
+<div align="center">
+    <h1> rustic_ml </h1>
+    <p><i>A machine learning library created from scratch</i></p>
+    <p><strong>Created by Kjetil Indrehus</strong></p>
 
-A machine learning library created from scratch with Rust
+   <br/>
 
-> Create being worked on as of 25th of May, 2024. Not public yet!
+   <!-- Badges Section -->
+
+   <p>
+      <img alt="Rust" src="https://img.shields.io/badge/rust-1.74-orange?logo=rust" />
+      <img alt="version" src="https://img.shields.io/crates/v/rustic_ml" />
+      <img alt="Downloads" src="https://img.shields.io/crates/dv/rustic_ml" />
+   </p>
+
+   <h4>Status</h4>
+   <p>
+      <img alt="Build" src="https://github.com/KjetilIN/rustic_ml/actions/workflows/test.yml/badge.svg" />
+      <img alt="docs passing ci" src="https://img.shields.io/docsrs/rustic_ml" />
+   </p>
+</div>
+
+
+## Summary
+
+`rustic_ml` is a machine learning library designed to be easy to use, and give the developer a flexible API to work with.
+This library is built of first principles, and the goal is to avoid any dependencies. 
+
+
+> ⚠️ This library is in the prototype stage. Breaking changes can happen. 
+
+
+## Table of content <!-- omit in toc -->
+
+- [Summary](#summary)
+- [Feature list](#feature-list)
+- [Usage](#usage)
+- [Use Cases](#use-cases)
+  - [Binary classification](#binary-classification)
+- [Macros](#macros)
+- [Feature Flags](#feature-flags)
+- [Deeper Reading](#deeper-reading)
+
+
+## Feature list 
+
+The library includes the following key features:
+- `Matrix` implementation
+- `Dataframe` implementation 
+- `Perceptron` binary classifier
+
+## Usage
+
+`rustic_ml` has documentation on docs.rs. It will be very useful to read it through
+https://docs.rs/rustic_ml/latest/rustic_ml/
+
+Run the following Cargo command in your project directory:
+```terminal
+cargo add rustic_ml
+```
+Or add it to the Cargo manifest. Make sure to pick the newest version:
+
+```toml
+[dependencies]
+rustic_ml = "0.0.2"
+```
+Also see the [./examples/](examples/) folder for different examples. 
+See also the specific use cases in the next section of the README file.
+
+## Use Cases
+
+### Binary classification 
+
+`rustic_ml` has implemented the `Perceptron`. It works well when you know your data is linearly separable.
+In the example below, we use a Jupyter Notebook with Rust kernel. This makes it easy to build up models with Rust:
+
+![image](https://github.com/user-attachments/assets/29ef6f0c-ab6f-46f9-bc2b-1b748c34e039)
+
+(See the full demo [examples/notebook_binary_classification.ipynb](examples/notebook_binary_classification.ipynb)
+
+
+## Macros
+
+> Coming soon!
+
+## Feature Flags
+
+> Coming soon!
+
+## Deeper Reading
+
+> Coming soon!