From 1cc1e4567329d552e4d9c3e7038bad6652bcd1c5 Mon Sep 17 00:00:00 2001
From: "Carlos A. Iglesias" <cif2cif@gmail.com>
Date: Mon, 2 Jun 2025 16:02:45 +0300
Subject: [PATCH] Update 2_2_Read_Data.ipynb

Changed image path
---
 ml1/2_2_Read_Data.ipynb | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/ml1/2_2_Read_Data.ipynb b/ml1/2_2_Read_Data.ipynb
index dc61966..6f4cf70 100644
--- a/ml1/2_2_Read_Data.ipynb
+++ b/ml1/2_2_Read_Data.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")\n",
+    "![](./images/EscUpmPolit_p.gif \"UPM\")\n",
     "\n",
     "# Course Notes for Learning Intelligent Systems\n",
     "\n",
@@ -34,11 +34,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to learn how to read and load a sample dataset.\n",
+    "This notebook aims to learn how to read and load a sample dataset.\n",
     "\n",
     "Scikit-learn comes with some bundled [datasets](https://scikit-learn.org/stable/datasets.html): iris, digits, boston, etc.\n",
     "\n",
-    "In this notebook we are going to use the Iris dataset."
+    "In this notebook, we are going to use the Iris dataset."
    ]
   },
   {
@@ -54,7 +54,7 @@
    "source": [
     "The [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), available at [UCI dataset repository](https://archive.ics.uci.edu/ml/datasets/Iris), is a classic dataset for classification.\n",
     "\n",
-    "The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, a machine learning model will learn to differentiate the species of Iris.\n",
+    "The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, a machine learning model will learn to differentiate the species of Iris.\n",
     "\n",
     "![Iris](files/images/iris-dataset.jpg)"
    ]
@@ -63,7 +63,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to read the dataset, we import the datasets bundle and then load the Iris dataset. "
+    "To read the dataset, we import the datasets bundle and then load the Iris dataset. "
    ]
   },
   {
@@ -180,7 +180,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Using numpy, I can print the dimensions (here we are working with 2D matriz)\n",
+    "#Using numpy, I can print the dimensions (here we are working with 2D matrix)\n",
     "print(iris.data.ndim)"
    ]
   },
@@ -218,7 +218,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In following sessions we will learn how to load a dataset from a file (csv, excel, ...) using the pandas library."
+    "In the following sessions, we will learn how to load a dataset from a file (CSV, Excel, ...) using the pandas library."
    ]
   },
   {
@@ -246,7 +246,7 @@
    "source": [
     "## Licence\n",
     "\n",
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]