diff --git a/ml1/2_1_Intro_ScikitLearn.ipynb b/ml1/2_1_Intro_ScikitLearn.ipynb
index 1dd3c15..0fd256f 100644
--- a/ml1/2_1_Intro_ScikitLearn.ipynb
+++ b/ml1/2_1_Intro_ScikitLearn.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -87,7 +87,7 @@
    "metadata": {},
    "source": [
     "Scikit-learn provides algorithms for solving the following problems:\n",
-    "* **Classification**: Identifying to which category an object belongs to. Some of the available [classification algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are decision trees (ID3, C4.5, ...), kNN, SVM, Random forest, Perceptron, etc. \n",
+    "* **Classification**: Identifying to which category an object belongs. Some of the available [classification algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are decision trees (ID3, C4.5, ...), kNN, SVM, Random forest, Perceptron, etc. \n",
     "* **Clustering**: Automatic grouping of similar objects into sets. Some of the available [clustering algorithms](http://scikit-learn.org/stable/modules/clustering.html#clustering) are k-Means, Affinity propagation, etc.\n",
     "* **Regression**: Predicting a continuous-valued attribute associated with an object. Some of the available [regression algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are linear regression, logistic regression, etc.\n",
     "* **Dimensionality reduction**: Reducing the number of random variables to consider. Some of the available [dimensionality reduction algorithms](http://scikit-learn.org/stable/modules/decomposition.html#decompositions) are SVD, PCA, etc."
@@ -105,7 +105,7 @@
    "metadata": {},
    "source": [
     "In addition, scikit-learn helps in several tasks:\n",
-    "* **Model selection**: Comparing, validating, choosing parameters and models, and persisting models. Some of the [available functionalities](http://scikit-learn.org/stable/model_selection.html#model-selection) are cross-validation or grid search for optimizing the parameters. \n",
+    "* **Model selection**: Comparing, validating, choosing parameters and models, and persisting models. Some [available functionalities](http://scikit-learn.org/stable/model_selection.html#model-selection) are cross-validation or grid search for optimizing the parameters. \n",
     "* **Preprocessing**: Several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. Some of the available [preprocessing functions](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing) are scaling and normalizing data, or imputing missing values."
    ]
   },
@@ -128,9 +128,9 @@
     "\n",
     "If it is not installed, install it with conda: `conda install scikit-learn`.\n",
     "\n",
-    "If you have installed scipy and numpy, you can also installed using pip: `pip install -U scikit-learn`.\n",
+    "If you have installed scipy and numpy, you can also install using pip: `pip install -U scikit-learn`.\n",
     "\n",
-    "It is not recommended to use pip for installing scipy and numpy. Instead, use conda or install the linux package *python-sklearn*."
+    "It is not recommended to use pip to install scipy and numpy. Instead, use conda or install the Linux package *python-sklearn*."
    ]
   },
   {
@@ -156,7 +156,7 @@
    "metadata": {},
    "source": [
     "## Licence\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."
    ]