[QuantumClassification] Add tutorial on feature engineering (#856)

Adds classification tasks used in [Q# Coding Contest 2020](https://codeforces.com/contest/1357/).

tutorials/QuantumClassification/ExploringQuantumClassificationLibrary.ipynb

@@ -14,7 +14,8 @@
     "This notebook offers a high-level walk-through of solving classification problems using the [quantum machine learning library](https://docs.microsoft.com/azure/quantum/user-guide/libraries/machine-learning/intro) that is part of the Microsoft Quantum Development Kit. \n",
     "It does not require any familiarity with the basic of quantum computing to follow.\n",
     "\n",
-    "The companion Q# notebook [Inside Quantum Classifiers](./InsideQuantumClassifiers.ipynb) offers a deep dive in the internals of a simple quantum classifier and several exercises on implementing it from scratch. \n",
+    "* The companion Q# notebook [Inside Quantum Classifiers](./InsideQuantumClassifiers.ipynb) offers a deep dive in the internals of a simple quantum classifier and several exercises on implementing it from scratch.\n",
+    "* The Python + Q# notebook [Quantum Classification With Feature Engineering](./QuantumClassificationWithFeatureEngineering.ipynb) continues the high-level exploration of the quantum classification library, focusing on using feature engineering.\n",
     "\n",
     "> <font color=\"red\">This notebook contains some heavy computations, and might take some time to execute. \n",
     "    Precomputed cell outputs are included - you might want to study these before you opt to re-run the cells.</font>"
@@ -298,6 +299,14 @@
     "\n",
     "**Go on to the [deep dive tutorial](./InsideQuantumClassifiers.ipynb)**.\n",
     "\n",
+    "## Next Step: Feature Engineering\n",
+    "\n",
+    "Similarly to the traditional machine learning libraries, quantum classification can take advantage of feature engineering, modifying the input data to enable classification of more complex classes.\n",
+    "In the next part of the tutorial, you can find several examples of feature engineering used with the QML library, and solve exercises on picking the right parameters to distinguish classes of increasingly complex shapes.\n",
+    "\n",
+    "**Go on to the [Quantum Classification With Feature Engineering tutorial](./QuantumClassificationWithFeatureEngineering.ipynb)**.\n",
+    "\n",
+    "\n",
     "## What's Next?\n",
     "\n",
     "This tutorial covered classifying artificial data, taking advantage of its simple structure. Classifying real data will require more complex models - same as in traditional machine learning.\n",