From 77ed6c91bebb7d83c27c646438e628703d1133d9 Mon Sep 17 00:00:00 2001
From: "Carlos A. Iglesias" <cif2cif@gmail.com>
Date: Thu, 9 Apr 2026 11:51:47 +0200
Subject: [PATCH] Fix typos and improve clarity in markdown cells

---
 ml3/2_4_1_Exercise.ipynb | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/ml3/2_4_1_Exercise.ipynb b/ml3/2_4_1_Exercise.ipynb
index 9169513..96e458a 100644
--- a/ml3/2_4_1_Exercise.ipynb
+++ b/ml3/2_4_1_Exercise.ipynb
@@ -197,7 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The features are simply the position of each point in the 2 dimension plane.\n",
+    "The features are simply the position of each point in the 2-dimensional plane.\n",
     "\n",
     "In other words, a point $\\mathbf{x}$ is represented by its values $x_1$ and $x_2$:\n",
     "\n",
@@ -208,14 +208,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Perform the classification task on several classifiers"
+    "## Perform the classification task on several classifiers."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Following, the classification on the spiral is done with several classifiers. We can see the performance on each class (each spiral), and their decision surfaces."
+    Following the classification on the spiral is done with several classifiers. We can see the performance on each class (each spiral), and their decision surfaces."
    ]
   },
   {
@@ -266,7 +266,7 @@
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
-    "lr = LogisticRegression(n_jobs=-1)\n",
+    "lr = LogisticRegression()\n",
     "lr.fit(X,y)\n",
     "\n",
     "lr_preds = lr.predict(X_test)\n",
@@ -275,8 +275,8 @@
     "print(classification_report(y_test, lr_preds))\n",
     "\n",
     "plt.figure(figsize=(10,7))\n",
-    "# This methods outputs a visualization\n",
-    "# the h parameter adjusts the precision of the visualization\n",
+    "# This method outputs a visualization\n",
+    "# The h parameter adjusts the precision of the visualization\n",
     "# if you find memory errors, set h to a higher value (e.g., h=0.1)\n",
     "plot_decision_surface(X, y, lr, h=0.02) "
    ]
@@ -535,11 +535,11 @@
     "collapsed": true
    },
    "source": [
-    "We see that some classifiers (kNN, SVM) successfully learn the spiral problem. They can classify correctly in any part of the plane.\n",
+    "We see that some classifiers (kNN, SVM) successfully learn the spiral problem. They can classify correctly at any point in the plane.\n",
     "\n",
     "Nevertheless, some classifiers (Logistic Regression, Gaussian Naive Bayes) are not able to learn the spiral pattern with their default configurations.\n",
     "\n",
-    "In particular, the MLP performs very bad: it is not able to learn the spiral function. Nevertheless, it should be able to."
+    "In particular, the MLP performs very badly: it is not able to learn the spiral function. Nevertheless, it should be able to."
    ]
   },
   {
@@ -578,7 +578,7 @@
     "- regularization of the network\n",
     "- new features that are passed to the network\n",
     "\n",
-    "You can search inspiration on [this playground](http://playground.tensorflow.org)."
+    "You can search for inspiration on [this playground](http://playground.tensorflow.org)."
    ]
   },
   {
@@ -621,7 +621,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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",
     "© Óscar Araque, Universidad Politécnica de Madrid."
    ]