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@ -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."
]