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cif2cif 2016-03-29 12:57:26 +02:00
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ml2/3_0_0_Intro_ML_2.ipynb
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@ -27,7 +27,7 @@
"source": [ "source": [
"# Introduction to Machine Learning II\n", "# Introduction to Machine Learning II\n",
" \n", " \n",
"In this lab session, we will go deeper in some aspects that were introduced in the previous session. This time we will delve into a little bit more detail about reading datasets, analysing data and selecting features. In addition, we will explore the machine learning algorithm SVM in a binary classification problem provided by the Titanic dataset.\n", "In this lab session, we will go deeper in some aspects that were introduced in the previous session. This time we will delve into a little bit more detail about reading datasets, analyzing data and selecting features. In addition, we will explore the machine learning algorithm SVM in a binary classification problem provided by the Titanic dataset.\n",
"\n", "\n",
"# Objectives\n", "# Objectives\n",
"\n", "\n",

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ml2/3_4_Visualisation_Pandas.ipynb
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@ -4730,7 +4730,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Feature Engineering is the process of using domain/expert knowledge of the data to create features that make machine learning algorithms work better. We are going to define several [new ones](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/)." "Feature Engineering is the process of using domain/expert knowledge of the data to create features that make machine learning algorithms work better. We are going to define several [new ones](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/) in the exercise."
] ]
}, },
{ {