Typo

ml2/3_0_0_Intro_ML_2.ipynb

@@ -27,7 +27,7 @@
    "source": [
     "# Introduction to Machine Learning II\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",
     "# Objectives\n",
     "\n",

ml2/3_4_Visualisation_Pandas.ipynb

@@ -4730,7 +4730,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "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."
    ]
   },
   {