Not done reviewing ml2 yet

ml2/3_6_Machine_Learning.ipynb

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+   "source": [
+    "![](images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Course Notes for Learning Intelligent Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous session, we learnt how to apply machine learning algorithms to the Iris dataset.\n",
+    "\n",
+    "We are going now to review the full process. As probably you have notice, data preparation, cleaning and transformation takes more than 90 % of data mining effort.\n",
+    "\n",
+    "The phases are:\n",
+    "\n",
+    "* **Data ingestion**: reading the data from the data lake\n",
+    "* **Preprocessing**: \n",
+    "    * **Data cleaning (munging)**:  fill missing values, smooth noisy data (binning methods), identify or remove outlier, and resolve inconsistencies \n",
+    "    * **Data integration**: Integrate multiple datasets\n",
+    "    * **Data transformation**: normalization (rescale numeric values between 0 and 1), standardisation (rescale values to have mean of 0 and std of 1), transformation for smoothing a variable (e.g. square toot, ...), aggregation of data from several datasets\n",
+    "    * **Data reduction**: dimensionality reduction, clustering and sampling. \n",
+    "    * **Data discretization**: for numerical values and algorithms that do not accept continuous variables\n",
+    "    * **Feature engineering**: selection of most relevant features, creation of new features and delete non relevant features\n",
+    "    * Apply  Sampling for dividing the dataset into training and test datasets.\n",
+    "* **Machine learning**: apply machine learning algorithms and obtain an estimator, tuning its parameters.\n",
+    "* **Evaluation** of the model\n",
+    "* **Prediction**: use the model for new data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "![Machine Learning Process from *Python Machine Learning* book](images/machine-learning-process.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Licence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Licence"
+   ]
+  },
+  {
+   "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",
+    "\n",
+    "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
+   ]
+  }
+ ],
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