diff --git a/docs/14_machine_learning_basics/arfpy_lesson.ipynb b/docs/14_machine_learning_basics/arfpy_lesson.ipynb
new file mode 100644
index 000000000..e849025ac
--- /dev/null
+++ b/docs/14_machine_learning_basics/arfpy_lesson.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ARFPy Lesson\n",
+    "\n",
+    "Welcome to this ARFPy demonstration. In this lesson, we'll walk through some basics of using the `arfpy` library for machine learning tasks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure arfpy is installed\n",
+    "!pip install arfpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to ARFPy\n",
+    "\n",
+    "`arfpy` is a powerful library used to perform automated machine learning tasks with minimal code. It provides various tools and functionalities to train, validate, and deploy machine learning models efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Training a simple model using ARFPy\n",
+    "\n",
+    "Let's look at a quick example on how to use `arfpy` to train a machine learning model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arfpy as arp\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Load iris dataset from sklearn\n",
+    "data = load_iris()\n",
+    "X, y = data.data, data.target\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize an ARFPy model\n",
+    "model = arp.Model()\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f'Model accuracy: {accuracy * 100:.2f}%')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/14_machine_learning_basics/readme.md b/docs/14_machine_learning_basics/readme.md
index 6ae7638f4..a01af6783 100644
--- a/docs/14_machine_learning_basics/readme.md
+++ b/docs/14_machine_learning_basics/readme.md
@@ -5,7 +5,8 @@ In this chapter we will introduce the basics of classical machine learning. We w
 To get a wider perspective on available algorithms and methods, the reader is referred to the [scikit-learn documentation](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning) and [Digital Sreeni's YouTube channel](https://www.youtube.com/c/DigitalSreeni).
 
 ## Python libraries used in this chapter
-We will use [scikit-learn](https://scikit-learn.org/) which can be installed like this:
+We will use [scikit-learn](https://scikit-learn.org/) and [arfpy](https://pypi.org/project/arfpy/) which can be installed like this:
 ```
 mamba install scikit-learn
+pip install arfpy
 ```
diff --git a/requirements.txt b/requirements.txt
index 297915fd5..d7c7a2d63 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,6 @@
+plaintext
 jupyter-book<2.0.0
 sphinx-book-theme<1.0.0
 matplotlib
 numpy
+arfpy