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Update 2_1_Intro_ScikitLearn.ipynb

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ml1/2_1_Intro_ScikitLearn.ipynb
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@ -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."
]