From fdd4ed770b1c1ec1cc053dd4d62b9d6c849581e0 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 4 Jun 2021 10:53:25 -0400
Subject: [PATCH] video callouts and clustering edits

---
 Classification/1-Data/README.md              |   2 +-
 Clustering/1-Visualize/README.md             |   4 +-
 Clustering/2-K-Means/README.md               |  30 +----
 Clustering/2-K-Means/solution/notebook.ipynb | 134 ++++++++++++++-----
 Introduction/1-intro-to-ML/README.md         |   4 +-
 Introduction/2-history-of-ML/README.md       |   4 +-
 Introduction/3-fairness/README.md            |   4 +-
 README.md                                    |   2 +-
 Regression/1-Tools/README.md                 |   2 +-
 9 files changed, 117 insertions(+), 69 deletions(-)

diff --git a/Classification/1-Data/README.md b/Classification/1-Data/README.md
index 42f119fd..24dc19af 100644
--- a/Classification/1-Data/README.md
+++ b/Classification/1-Data/README.md
@@ -1,7 +1,7 @@
 # Introduction to Classification
 
 [![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
-> MIT's John Guttag introduces Classification
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
 
 ## [Pre-lecture quiz](link-to-quiz-app)
 
diff --git a/Clustering/1-Visualize/README.md b/Clustering/1-Visualize/README.md
index a665e9b5..779deab0 100644
--- a/Clustering/1-Visualize/README.md
+++ b/Clustering/1-Visualize/README.md
@@ -9,7 +9,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
 ### Introduction
 
 [![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
-> MIT's John Guttag introduces Clustering
+🎥 Click the image above for a video: MIT's John Guttag introduces Clustering
 
 [Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
 
@@ -309,6 +309,4 @@ Before you apply clustering algorithms, as we have learned, it's a good idea to
 
 [This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave, given different data shapes.
 
-In the next lesson, you will make use of the  most popular clustering method, K-Means. Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
-
 **Assignment**: [Research other visualizations for clustering](assignment.md)
diff --git a/Clustering/2-K-Means/README.md b/Clustering/2-K-Means/README.md
index 56c26987..b7668077 100644
--- a/Clustering/2-K-Means/README.md
+++ b/Clustering/2-K-Means/README.md
@@ -1,6 +1,4 @@
-# [Lesson Topic]
-
-Add a sketchnote if possible/appropriate
+# K-Means Clustering
 
 ![Embed a video here if available](video-url)
 https://www.youtube.com/watch?v=hDmNF9JG3lo
@@ -12,46 +10,24 @@ Describe what we will learn
 
 ### Introduction
 
-Describe what will be covered
-
-> Notes
-
 ### Prerequisite
-
-What steps should have been covered before this lesson?
-
 ### Preparation
 
 Preparatory steps to start this lesson
 
----
-
-[Step through content in blocks]
 
-## [Topic 1]
-
-### Task:
-
-Work together to progressively enhance your codebase to build the project with shared code:
-
-```html
-code blocks
-```
 
 ✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
 
-## [Topic 2]
-
-## [Topic 3]
 
 ## 🚀Challenge
 
-Add a challenge for students to work on collaboratively in class to enhance the project
 
-Optional: add a screenshot of the completed lesson's UI if appropriate
 
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
 
 ## Review & Self Study
 
+Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+
 **Assignment**: [Assignment Name](assignment.md)
diff --git a/Clustering/2-K-Means/solution/notebook.ipynb b/Clustering/2-K-Means/solution/notebook.ipynb
index 483198dc..0c68e7cd 100644
--- a/Clustering/2-K-Means/solution/notebook.ipynb
+++ b/Clustering/2-K-Means/solution/notebook.ipynb
@@ -35,7 +35,7 @@
   },
   {
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-   "execution_count": 4,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
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@@ -43,12 +43,12 @@
      "name": "stdout",
      "text": [
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-      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
       "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
       "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
-      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
       "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
       "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
       "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
       "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
@@ -66,7 +66,7 @@
   },
   {
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+   "execution_count": 82,
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    "outputs": [
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@@ -120,7 +120,7 @@
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   {
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+   "execution_count": 83,
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@@ -131,13 +131,13 @@
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      },
      "metadata": {
@@ -157,15 +157,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "ipykernel_launcher:12: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "     artist_top_genre  popularity  danceability  acousticness  loudness  \\\n",
+      "1                   1          30         0.710        0.0822    -5.640   \n",
+      "3                   2          14         0.894        0.7980    -4.961   \n",
+      "4                   1          25         0.702        0.1160    -6.044   \n",
+      "5                   2          26         0.803        0.1270   -10.034   \n",
+      "6                   2          29         0.818        0.4520    -9.840   \n",
+      "..                ...         ...           ...           ...       ...   \n",
+      "514                 0          20         0.838        0.0358    -3.723   \n",
+      "515                 0          14         0.786        0.1950    -4.232   \n",
+      "519                 1           2         0.879        0.2240    -4.602   \n",
+      "522                 0          26         0.863        0.0366    -3.130   \n",
+      "525                 0          10         0.735        0.6320    -2.582   \n",
+      "\n",
+      "     energy  \n",
+      "1     0.683  \n",
+      "3     0.611  \n",
+      "4     0.833  \n",
+      "5     0.525  \n",
+      "6     0.587  \n",
+      "..      ...  \n",
+      "514   0.931  \n",
+      "515   0.806  \n",
+      "519   0.916  \n",
+      "522   0.896  \n",
+      "525   0.918  \n",
+      "\n",
+      "[286 rows x 6 columns]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "wcss = []\n",
     "\n",
-    "X = df[['popularity','danceability']].values\n",
+    "X = df[['artist_top_genre','popularity','danceability','acousticness','loudness','energy']]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
     "\n",
+    "# X = le.transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n",
+    "print(X)\n",
     "\n",
     "for i in range(1, 11):\n",
     "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
@@ -176,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -190,8 +242,8 @@
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -209,22 +261,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "     popularity  danceability\n1            30         0.710\n3            14         0.894\n4            25         0.702\n5            26         0.803\n6            29         0.818\n..          ...           ...\n514          20         0.838\n515          14         0.786\n519           2         0.879\n522          26         0.863\n525          10         0.735\n\n[286 rows x 2 columns]\n"
-     ]
-    },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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ojGbIhwI3Fj9BCJEEZEgpVeAZ4OvKnmhJZNYzQE7xI4ATmfkQoubcig3q3YmutRYm8B0l/CxPH7iWhXWmMNREWi4A12ICHyYWRMxd/n9J93pkzivg3QoiBqKGI2LuRQgj0nsInHOB4gZKgsxB2ichYu4tcx6rZ63DYAhOevK4PCz4cWlYBj39yEndh0JJ3A43K39dyzWPDmHN7+vLNOagxcLDfajsWrcPrye4otdkMXF4x1HqNk8OEPsqjfqt9KtF9Vg4aRnvjxiLy669/9QDaaz45S9Gr3qDhq3+24lfj513Pte2bsvi/fuIMpm4uGlz4iwWjuTm4PJ6aRifwJHcbNIddnLcLg7nZHPHzBnYvR7sHg82o4l3Vy5j2nU30iA+tDxuvtvNvL27yXQ6Oa9efc5JqvEvvsuznzItlJTSK4S4H5gDGICvpZRbhBCvAGullDOBvsAbQgiJFnK5rwrnrOFZrX9c3Y+ULoSogEqesSX4DhFk1KUHDHXB2IBgYa4QKOFrPouEd5A5/wPHL9r4htqIuFEIk+aNSu9uZMYtQMGmqcyB/C+Raioi/jWkZxugF67wgfNPCMOg+3yqbuWhlIRlcAH+nLxSV4K2JEJAtdra5xNbPYbj+0+Uer7FZqZd7zY0bd8orHk079SYrSt2Bhl1j8tDvZZ1SG5ci5YpTdm2ehfeUgy7xWbmrneGh3VPn8/Hpw994zfmAKpPxZnn4tvnJ/HStMfDGud00iA+geHtOwJaT9Abp/3EnswMFCEQQuBVVf/G6M6MgO0xHF4PLp+XZxbMZcJV1+mO/8+xo9zyyzRUKfGqKooQXNq8JW/1u+SM34f4rxBWHrqUcraUsoWUsqmU8rWCYy8WGHOklFOllM0LzhkhpW4MoZIJbVgrensRcx9QsuLTBlHXI5QYhLAhEsYUnGMFTGjRxJIfow2ibgv/vsKKEv8aotY6RM2ViKQFCEtRVofM+5zgUJATHDORagbIPELEgfRj8zp0G9TRn71RHLPNTJ9re4Q1hjPfFZbxN9vMXHH/QACueXRIUN65waRQvW4i8UlxJNWrztCnr+Dln58Iaw4AVz00GJO1hHKizUyXAR39m7yjZjxB+z6tMVlM2GKsxFSL5vqnLqdFSlNiqkXTukcLXvvtWTr3bx/WPTNTswNi9oVIKdm8fFvYcy9kd8ZJVh0+RI7rX/hTKoFXVbl+6k9sP5mOy+fD4fVi93h0s1yKo0rJX0cO657nU1VGzvqFPLfbP5bT62X2rp38vvu/32P0WG4uzy+czwXjv+L6qZNYsG/P6Z6SLmdupaiIKTBkJTEgRHTFhjS1hsSv/FkuiHiIvg0RPbLY8LVBSQQ1Hc2QWzTv3XdEC81IN0TdgLCVbyMRCio+9bRXvNvQfYAJE3gPIZQEJIr+OUp4G1bVaiVwz4e3MfaRb/F5fag+FbPVTP/hvUk/fJIZH8+mdY+WtExpGnKMLgM7MPHNGUF6L0JopfsGkwGfV+Wud2+mbc9zALhgaE/2bz7ItA9mYbKY8Lq9NOvYmFdmPkVcYmxYcy9JzQY1+HDp/xjzwFdsWaFluQwa2Y/bXr3Bf05cYixvznmBzNQscjPzqdus9ilVu0bHR4VcnVQrR/58mj2fETNnsCvjpD9V8MGu53FPl3+vUnLpwf3kud0V0l8XCF1JgI2pxwOqXQuxez1M3rKJQc1bVGiu/wapeXkMnvgdeW43XlXlQHYWm0+k8th553N7x86ne3oBnLkGPeYhyH2DQCMmwHaDP8UwHKTvJNIxGbw7wHguIuoalCT9NHopfVroQ00nwCP2HYGE0VqYx9QirE3IcmE8B7y7CTLY0g3G+mh71kYCY+gAFrAODvs2l47sT8cL27Jo0nI8Tg+N2zVk9P1fsmDCUrxuH4pBoVO/c3lp6uO6xq9F56Y069SYLcu2BxzvcEFb7n7/VnIz8mjRpSm26KJVkBCC21+7kWseG8LeDQdIqptIvRanrqQXHR9FUt1EYhKiiIrVfjaagudcrVYC1WqVryWaHrZoKxfeeD6LJi0P2EOwRFu48Zkr/f8+lpvLOyuW8ueBfUSZzAxr14E7OnbGWKAFc8+sX9iWdgKvlP5cqjFrVtEyqQYXNq68Aq7SOJGXhyrL37vVKAR9GjX2v5fieKVKqKiKR/1vN6v47O+/yC8w5oU4vF7eW7mMG9q2828c/xc4Yw26iBquhRvyv0ILe6hguxIR90zYY0jPLmTGUM0w4gIWIvPHQfVpCKPOJpZ7lZYSGBTe8IBrCSL+hQq/n9IQ0XchnfPwx9ABsILtUv/DQ8bcC3mfFzvHCsYGiKgrQo4rpWTbqp2snr2OqFgbFwztSd1myQx7/hqklNze+mFyT+YGdHBfN38Tv42bz2X3XhI03rF9qexaG7wU3bJiB7GJMTRp11B3HqkH0pjw2jQ2/rmFGvWTuOHpK+nUr+Jl/pmpWdyT8iT5mfmoqiTnZB7fPD+JA1sP8+i4uys8blk8+MkIPC4PS6etxmg2IFXJsBevpe/1mp5OltPBZZN+IMvpwCclmU4nH61ewba0E3w4YDCHc7LZkpaGt4Rn7PB6+fKftZVu0LOcDranp5McExug29IxuU6oAF4AArAYjahSYlIUqtuieP3C/rrntq+VrOu524zG/7zY1opDB3V12Q2Kwu7MDM6teeq1GpXFmWvQhUDEPoKMvkvzkA21EEr5+kvKnBdKxJ+dIN3I3DcQ1T4JvkDNAl3PxQeOn1AdE8DYDBH7LMISXtw5HISpOSSOL8hy2QIiFqJuLoj5aygx9yJN7ZH270HNAesliKjrQqpASil557ZPWDp1FU6HC6PJyHcvT+HJb+6jz3U9OLY3lbRD6ZRcdbvsLmZ/qW/Ql05dpSusBbBs+mqufHBQ0PFj+1K5p/OTOPOc+LwqR3YdZ9uqXdz38W0MvP2icnxKRfzyyR848wKFtFx2F/N/WMLwUdeRVEd7CG5ZsYMZo2eTcSyT7pemMHhkP6LjokodW0otfdJoNlKjXmA4y2w188wPD3Hfx7lkHMsiuUnNgBZ2EzdtJN/jxlfsQ3V6vczZs4tD2dlku5yYDAouHYc1w273/7wh9Tiv/LmQjanHiTVbGN6+A/d3PU/XMw71Ht5esZRv16/DbDDgUVU61Erms0svJ85ioUX1JC5q3ISF+/b6884tBgMxZgte1UeOy0WrpBo817svPlVr4NwoIYE+DfW9c9CUEz+6ZDD3zp6JKiUun48ok4mU5LqVqktTFdSOiWVXiU1gAI/PR42o0r8v/zZnnEGX0gPSCSJGM+pKFCjNKzCOVytICvJFVHAt1b/G1IlAL7k4Bcts705k5t2Q+C3C3CnwejUPhFmLlZcTYe6ASJpe+jmWnghLeOqKa/5Yz9Jpq/wVmYXZHu/c/gldBnbE6/GFzDzwevSXyF6PTzeOrKoy5DU/vDIVR64zYEPWZXfx+WPf0f/mPiF7fUop2bpyJ+lHMmiR0iSgonXT0m36ErwWE/s2HiCpTiK/fTGPsY+Mx+1wIaVWjTrrs7mM/fstouP192C2rtzBGzd9ROaJbKQqqX9OXV6Y/Ch1mwXmmcclxurG/9ccPaIbRzYZDGxNP0Hfho2DHqCgGcOLmmh7F3szM7hx2mQcBborWS4n49at5XheHm/2C37I6jFj+1a+2/APLp8PV8EG5rpjR3l87u+MG6Kt6D68ZDCTtmxiwqYNuLxehrQ4hxGdUojRyTM/v4H+yqskfRo1ZuHwO/hlxzZOOuz0atCIHvUbVLkM76lyV+curD16OKDwyqwY6Fq3PrVjKrbPU1WcMQ0upPSg5ryGTO2MPNENmdZH66PpmI2a+x7SPh0pQxlbPRS0LEwdQhhc4dtLeM9AJzLvo6K5u/9BTRuEPNEFmdoRNethpFq1jRHKYsGEpbrqgQajgXXzN1K/ZR1iE4MbSJhtZvrfrK+r0uPyLhh04tSKIuhxuW5hGxsWb9HNrvH5VI7v009nPHkskzvaPMzTA17l/RFjGdHmEd6941N/dWuDc+qi6OTUe91eajWqidPu4rNHx+Oyu/wG1O1wk34kg18+naN7z8zULJ6+5FWO70/DZXfjdnrYu/EAj/Z+UTfnXY+miYmYdDxYn6p1s7cYjYzqcwE2o5FCE2cxGEi02RjRUfv8Plv7F25f4P2cXi8/79jGyWJefGl8sW5tUFWoW/Wx5OB+sp1a5N6gKNx0bntm3zicBcNv5+HuPXSNeXmpFRPDyM5deOb8PpzfoOF/3piD1jnpxd4XEmM2E20yYTYY6FG/AaMHXnq6pxbEGeOhy5yXwTETf9m9ehyyH0FiBlxIoiDvHUicHJaglRAK0joAnH8QmMNtKehKpIOaBcICMow/YK+WiiW9h5CZtxYJb+ED5zykZwfSUB/wIGxXgnVwuTZzTxWDMfSzXDEoCCF4buIjPDPgVXxeH26nB1uMlQat6nLFAwN1r2vUpj7XPDaEae/96i/cMZmN3PDsVQFe7OFdx5j+wSz2bzkUMg/c5/ERl6Tv/bx2wwcc2XU84EGw+KcVtOrWnMEj+3PVw4OZ9/2SgGwbk8VIyy7NsEZb+Ov3f3RXDB6Xh6VTV3LjM1cFvTb3u8VBKZlSlTjyXfz1+z/0uKyL7lyLM7xdR37ctDEgHmtSFJpXr07rGppkxdWt29K4WiJf/fM3x/Ny6dOwMcPbdyDBqmnfbEk7ERCyKcRiMHAgO4vqYYQACo12SRQhyHO7ibdakVKyaP8+Jm7eiNPr4bKWrbiiZStMhn/vO1qZ7M3MYPmhg8SaLfRv0rTcFa3Xtz2XK1u1Zn9WJtVsNmpEVSyTrqo5Iwy6VHPBMYPg4hlJUX62HVQnMudZROL3YY0r4kYhvfvBtwcQWmMIc0dE7CP6F5g7h2fMAYzaElnavy/YdC2OR7unT9tAlO514JwFCZ9XaYGFVLUeo3jWcPNjNrYsExzdK0ucI/0bkm16tOS7PWNYMGEpaYczaNerFd0u7YShlD/q214ZSq+rurF06iqEEPS57jwan1u0JN+yYgdPX/I/PC4PPq+q+2AxWU10G9RJN2yRmZrF9tW7g7x6l93Fz2N+Z/DI/tRvWZfXZj3DeyPGknZYi32269uGEwfSua3lg0gpQz5I7Ln6xu7EwXR/H9XieFweTh7JCPl5FKd+fDzjr7iap+bP4XCOplLYt1Fj3ioRKumUXIdOIXpmtkqqwc6T6UFG3eXzUT8+Pqx5nN+gITO2bw0aI8ZsJjlW+8xfX/onEzdv9Evqrjt2jOnbtvDDlddiqKLuTFWBlJJXlixi0uZNgMSoKLy4eD5fX3YVKXXCrwIGLfTVonp4onunizPCoKOeQL85c9CJ4F6LlO6w4tRCiYXqU8GzEXz7NYGvUnRghKE2MuoWsH9PUSzdiKa8WPyPw4qIeVD70bOx4PXScIBrtZZFYzmvzHkXIj07kM55CGEE60CEMXQsU6qZyJNXgS8DcFCrhplxCyXP3NCU7euiC7RX4IUpj2GNKtrIS6gRz9UPl29p2axDY5p10C/T/+CuzwNCPT6vZpgVg4IlyozX7aPLJR148lv9YmNnvgvFoP/QK17Y075vG8bvGk3OyVwkcEerh8jNyNONURcn1AO1dkP9EnWv21sueYCUOnWZf/NtZDmdWIxGosqZ8nZ3Sld+370zIGRiNRoZ2KyF32uUUjJp80bGrv2LfI+bvg0b83zvvlSzad77w917sGDfHvLdHtyqD0UILAYDr1/YH0UIDmVn88Om9f74OmiVoJtOpLJo/176NSmfTv/pZPGBfUzZshlXQZiq8D2N/PVnVo+4+4xdcYTijDDoEgMhKyFPESEEmNsD4VUEitjHwNxZk82VeWAZBDIf7N+CzARDE0TccwhzwRI8yDsPhQPpWooI06Crue9D/reAB4mAvE+QsU+hRA/TPV/mjgHfCYpWOW5MZnhjci6/TRmJNcbG+Vd1rXAxTzg47S4ObT+i+5rJauKjZa+SUCuBajVDe5q1G9cktloMLnugV2w0Gzm/RL9RIQTxSXEsmLAUj8tbpjEHaHyuvqxz5okc3eOKQeHEAU23XUrJr2Pn8OMbM8hKzaZh63rc9d4tdLoosD2dEIJqNpvecGXSLLE6P1x5Lfiea90AACAASURBVC/9uZAtJ1KJNpsZdm4HHulelFV13++/8kex6ssZO7bx+55drLz9LuKtVurExjHp6ut5buE8tqWnUd0WxRM9e/kN9crDB1GEQklHxO7xsHDfmWXQJ2/Z5F9lFMerqvx97Cjdz7LmHGeEQUeG20vSAOaeFcoiCRchBFgv0BpbFCf2Hv1mCkq4sTYzKOFVFErP1gJjXvxz8ULuW0hrP4ShdvBFrjno6b2YDOlc+UAXhKHqc2lN5tBfN4OiBIRmQiGE4Mnx9/PCZW/h83jxenxYoizE14jVjX0DpB/J0A2XBA8Ol96ln0ett3EL2nsqjMdPenMGP74+3b8C2bvxAC9e9iZvznmetudXXmpex+Q6zBw6TPf7djgnO8CYF+L0evnfkkW8e/FAUvPyuHH6FPLcLlw+H05vDk/M+4Nok5m+jRoTb7XqblYaFYXECj6IThch5QqElnZ4tnGGBMPKeu4YQUSDUhMR/5r/qJQe1PzvUdOvQE2/HDV/PDJsj7n86C7XzX2AcB4wmkek5n6EdK3QFcoqRDr/ILgqFECAa1HRee5/UDPuQk0boK0idFG1jd5/iwqUk6uqyrzv/uSB857lznaPsnXlDj5e8SqXPzCQnld0ZcSbN/HFxveJq66/ujinWzNMlrJ9F6PRwK51+3Rf63V1dyxRwb9HVZV0HdQRj9vDxDdmBGUOuRxuvnlhUuAxr5d1x46y82R6qb/nQranp/Hg77Po9/033D/7V7aladk/et+3GdtD68Ys3q+9t49WryDb6fCHH3xS4vR6eXrBHFQp6duwMUZF36Bf07ptmfP9L3HFOa2JMgaHtVQp6VK3fDH0M4EzwkMXxnpaWEFX2rY+IuYGMDQES1+E0H55Ukpk5l3gXovfk819D+lcAInj/zV1NxF1HdL+HagnKfKQzYDQtNURgE8rWMr7FHAi7VFg6gDVvvC/n0CUguuC7uY/Lp0LkFmPoG0ayxDnG8DUCVEOZchwyEzNYvVv60AIul/aiYQaWgjF7fKEDJz5fCpH9xwnPik2KA/8w7vGsWjSMr+x/HH3dBZPWsEna9/ydyAqjXa9W9OySzO2r96FqxRpX6/Hx7Lpqxn6VHB1bcsuTbFGWwPUFAEatq5LUp1E0o+cDOnFH9h62P/zzB3beG7hPIQQ+FSVOrFxfHXZlQGSs5tOpDJ+/TqO5eXSNDGRqVs241ZVVCnZn5XJov17+faKq+lSJ7iauZpVv5AMIN/j5sLvvuJ4Xl5QNSpAjsvFsdxc6sbF8f2V12ryuB4PQmgG8J1+l9Aooer7ulYmg5q14Jft21h15BB2jweTomBQFN7tPwCrjqE/0zkjDDpqDoQy6DIH6VwExtYIc2cQBToqnrXgWUdgWMIJ3o2lbj5K6QbnbKRrJRjqIKKuLVfXn5IIJQ6SfkbmfQbO+aDEIqJvQVqGILwbtEKprMeA9GKTsIN7HdI+BRF9Y/CY1sFad6KgzVYVLP20h1nOqBLvvfCzU6CwelSpiUh4r8LvTY/fv17AmPu/8ueBj77vCx754m763dQbW7SVus2TObzjaNB1HreHuzo8js/r4/yruvPYl3djsVk4svsYCyYsCQiZuJ0eju8/wZ+TV9D/5j5lzkkIweu/P8fPH8/mj68X4nK4yTiWqZu6GF9D38tfOXOtrs77oe1H2bvxAPVa1kHoeLUA9Vtq359taSd4esHcgOKifVmZDJsxhcW3jEARgl93bOepBXNw+3yoUrLq8KGAb70qJQ6vl5cXL2TWjcHSvte2bsuoPxfqCmu5fD72Z4XuO6OqkmizZuTa1qzFittHsj71GC6vj07JyVVmAFUpcXg8RJlMle5oGRSFL4ZcwfJDB1m8fy8JVhtXntOaunHlqyqvDBweD7/s2Maao0donJDAdW3OpWZ0cK3HqXBmGHRpR/Nq9WLp2eBZA541SMf3yMSfUMztwf23fuxdOsDzt65Bl2oe8uR1oB7131Pmfw3VPgt7s1IPoSQi4p6FuGeLjgGYU8CzFYleQYgTHNNBz6CbmiNj7oe80WiGWtH+H/syyCykayuooVLpohBxL4BSB8xdEKLyom7H959gzP1fBcWrP7jzMzpc0JakOok8PHYkz136Bh6X1mxZMSioPhXVq+L0ah748hma1v2zEx5i64qdBUJggWM68138PXdDSIOek5HLtPdnseKXNcQmxnDVw4O57onLue6JywG4s92jHNx2JMCrtkZbuPJBfTGz9Ys368rjAmxZvp0m7RpyzWNDmPLurwH57xabmVtevh6A7zeuD4rbqlKS6XCw7thR2tWqzfOL5gUY/FArmm3paboxdIvRyEeXDOahOb+VSy3RpCicV7+BP98dNGPYObkoLJHjcvHbrh2k5uXRObkOPU+xMEhKybh1axi75i/yPR4SrFae6HE+17U5t+yLy4EQgvMbNAy7orUqyHDYuXzSBDKdDuweDxaDgc//XsMPV11H+1o6e14V5Mww6IYGoMSAWtbmqAoZt0Ptv0FJQtMsL2EshbXgtSKkdIAvDemYVtDgovAPUvPIZPbjUGNppRq/IkoZs5Q/FiVmpFYY5VoIGJHGtpDzHDL3cMGYITYBDTW0QqYqYMmUlSG1XL59fhLndGtOhwvbMnrV60x5dyYHthwi/UgGGccDvUa308Oy6avJzczTGmHobdCZDdSor58TnJ+dzz2dnyTzeJa/Fd2udXvZ/eg+bn15KACvzXqGpwe8StqhkxiMBjwuDzc9fzUpF+tnOyXVTcRsNQU9rAxGg79Zx/CXrsOZ5+SXMX/gcXuJSYzm/o/voMMFWtw5NT9PtyhICEGGw87Ok+mE0R8EAJsxtDc7uEVLzm/QkImbN3IoJ4uJmzeFHCfWbMarqpyTVIMPLtEvGAPYfCKVa6dM9MfdFSFokVidGdffhMVYMTPyxbq1fLx6pT8F86TDzst/LsSsGOhUpw4JVhtxln9xf6cK+XDVClLz8/yKjYWyC4/Nnc38m2+vtPucEQZdCAXi30Jm3oeWj15aTnouqpqPsA6A3Nd1XBwFrJpIlJQ+ZO7bYJ8IQimo5tQL6+SBb5+/WOhUkdKFzBuneeD4QrhhNoTt2lLHEcYGYLxV21hL7w++w5TeUckG0XdWfOJl4PX4kDpxZLfTw8KJS1k8eTlSlQy84yKe+OY+hBAMa6zfTclgMpCdlkPHi9oSHW/Dme8M0IkxGA0MulNfvGvW5/PIOpET0FfUme9iyjszuerBwcRVj6Vmgxp8teVDdv+zj+z0XFp2aUpstdDL3/7D+/Lja4FaOkJo6ZZdB2maPf8s3Myvn83DV/AZuO1uPnvkW9r1bk2NetW5sHFTVh0+FFR27/H56JhcB6fHiy9M2dqy8tdjzGY61k6mZlR0SIMugNEDh1A7JqbUghkpJcNmTAnIS1elZPvJdN5evoQX+lwY1pxLjjl27eqgz8Lh9fL4/D+wGox4pcolTZvz5kUX/6ckaivCnD27A+R3Czmck0O63U5SJYl8nSFZLiAsvRBJv0DUjWAp4wsknVqHocTvQakH2EDYQKmDSBzvV2WUeZ+AfRKayqKeLG7heGpR3PkUkVIiM26D/C9APaJJGOChoI862jPWBpYeYNNPwwvCs75Ao13PGChaMxCsEH17hRpvhMt5l6XoarkAeFxevwbKnG8XsWrW3wC06XmOru6KoghqNaqBwWDgvUUv06hNfSw2M7YYKwk14nhp2hMBglzFWfPHet14t9FsZEcxeV8hBM07NSHl4valGnOApDqJvDrrGRKTE7DGWLFEmanbog7vLX4Zs8WElJL3RnyKy+7yh3HcTg+5mXmMf+knAK5u1Zp6cfFYDUV+lM1o4s5OXagRFU39+HiaJ1bHEEYY46TDHjJDZufJdHp+M447f/2ZUUsWhhyjXa3a9G7YqMzqxx3p6SE7J03eurnMuerh8nnJdetvUKtSYvdqXY3m7tnFk/P/qNA9/ktYQjRPkVJi1vn+V5SwPHQhxADgIzQ1qy+llG+WeL0BMB5IKDjnaSnl7EqbZeF9jI0Rcc8DoB5vSSgDLBRtY0uY2kCNBf4SewxN/ctUKSXYvyG0emIhChibIAyVlOLk+Vtr9BywH+ABojQ9d4MW28bUPvwNIjWDkM9mUzdE3FNgaIBQKncDppDDO4+yYfEWouJsITcGi+PMdzH7i/mcNySF4aOuZdWstTjznP5wjTXKwh2v34ipYIOuTtPajNvwHkd2H8Nld9OwTb1S5QdqNEhCKCJI+VH1qVSrpWXc+Hw+Jr35Mz+Pnk1+toM2PVpwzwe3hdRsB2jfpw0TD33Oga2HMZmN1G2e7P8dZRzPIjM1O+gan1flr9nrtPdlNDH9uhv5YdMG/ti9kziLheHtO3JR46KV37hLr+CWn6dyOCcHRdG0VfSIMZt1vx+qlNz68zRO5IdKU9WIMhoZd+nlpZ5TyAm7XmcwDVcFc7ktBiNJUVFlztPl8zFv7x4yHY4KF2O5vF6mbdvCnD27SLTaGNa+Q8DewL/BDW3aMXrNqoD9EYMQpNSpS5ylcpxFCMOgC00x6hOgP3AYWCOEmCml3FrstOeByVLKsUKI1sBsoFGlzVIPy0Bw6TwzjG0DCouEEGDUq2zzFHjlIRBRaKmFsYiE0ac83aLbbgqhB2MHYUXEVCAkYuoQoiLVBtaLtdZ6VYCUko/uHce875YghKYDE1YBD+ByavOt2yyZT9a8xfcvT2bT0m0k1a3ODc9cyXlDgtUZS8rUhuLKBwaydMrKgBRFxaCQ3KSWv9H0R3ePY+HEZf40xPWLtvDw+c/z+fp3SW4SushKURQatw2uJrVGW0K2oIuOL1pOR5vN3NW5C3d11hfzqhUTw+833cLWtBOk2+1M3LyBuXuDm4b0qK9f0bru2FFy3cHetACaVUukfnw8Peo35KZz24cd++5QKzlUjhmN4iuW8iqE4MkevXhh0fygsEtJTIrCSYe9Qgbd5fVyzZSJ7M3MwOH1IoC5e3fzRI9e3NqhU5nXVxYjOqWw7vgxlh86oDXdBpKionn/4uAeAadCOL/RrsBuKeVeACHEJOByoLhBl0BhHlA8EJyXVsmI+FeQJ7eD7yBaTN0ISjVEtU/Du16YNbVD38HgF40tEVG3gaEGmHtUrgqioU5R79EAbGGpROohDNWR0XeC/etiqo4WMNSusg1QgKXTVrHgh6W64Y3SsEZbuOjGXv5/12uezDM/PBTyfCkliyYuY+bYOTjzXfS9vieX3z8goJVdcVp0bspjX93LR/eMQ1UlPq+PJuc24KXpTyCEIPNENvN/WBqkme52epj8zi88NHak7rilER0XRcolHVg7Z32A8JclysIVOo09SkMIQZuCLjij/lyge86m1FTd43lut27miQSS4+L48rIww3jFiLNaGdy8JbN27QicJ4TsUBQOV7VqQ5TJzPurlnEkJweL0UiO06kbOKwfF57wWEmmbdviN+agfQ4Or5e3li/lqlZt/rVNV5PBwBdDrmBbehqbT6RSNzaO7vXqV7p8cDgGvS5wqNi/DwMlO9aOAuYKIR4AooF+egMJIUYCIwEaNND3MMJFy+/+DdxLwbMdjI3AcmG5yv5F3PPIzAcpCn8IwIKIG6XltFcFlgu0qlbpIDDmbUSKWLD/BOYUhM4GrJQ+LeYu4vxhpUKU2IeQpnMLOhZlg/USELHIk4ORvjQwNkXEPn1K6Zcl+W3cfF1NdT0MRgM+rw9rjJVW3Ztz0U29yr6ogI/uHceCH4r02w/tOMKiicsYvfqNkIVFFwztSa+ru7F/8yFiqkVTu1FN/2uHdxzFbDUFGXSf1xcQYy8vT357H89f+gZ7NhzAaDLgdnnoN6wXQ+6+uMJjHsrR15A5mperm7bYKbkObp2NaZvRxMBmFW/E/MElg6gbG8e3G9bh8vmoExPL0LbteH7RfPZnZ5EcE8uj3XsypOU55Rp3QLPmDGimNag5kJXFkEnfY/d4/CmXNqORR887v8KZNHP27NJdAZgNCuuOHaVvI30RuaqiVVINWiXpC71VBpWV5XID8K2U8j0hxHnA90KItlIGbtlLKccB4wBSUlJOWW1LCANY+mr/hUC6NyCdv4KUCNulCHPHoustfZEJH0POa6CmahuoCS9XnTGnYGUQ/y5k3kWAYqOQkPMi2kemIm2DEXGv+1MlVcfvkPNyQZhIRVouRMS/gSimFSOsFyKs2oaxmv8d5L5VdA/vNq1yNvGrIuGwUyQcz9xkNlKzYQ0uuKEnOSdz6TqwE10GdEAJU4L12N5U5o3/M7CwyOHh6J7jLJmykn7D9JttABhNRpp1DP6DTW5aSzc0pBgUmoQQ5wqH2GoxfLT8NfZvOUTq/hM07dCIpLrVy76wFGrHxHA0N7gZSq3oaN0YepzFwrPn9+aN5Uv8xUk2o4nm1atzxSm0ejMoCk+d35sne/bCJyXLDx7gntkz/THhA9lZPL1gDi6ft8LyAA0TEpg5dBgfrlrB6iOHqR0Tw70p3ejftOJiYIlWm264SJXyrEmJLE44Bv0IWlv5QuoVHCvOHcAAACnlSqE1skwC9FvO/EuoOe+C/Tu0fHKJdExFRt2AEvc0ANK7D7KfAOkCnFrWSdZDyOrT9QWuKgEp3ZB1P4Gbsd6C3qbFcP4O5h5gu0zTS89+ioCNVNdCZNYjiMRxOvfwQd7HBG/4OpG57yGqTwq6piJceFMvdv2zL6CQBrRsErPVhKIo9L2+B3e8cRMxCRVrCLB5+XZdzXRnvou1c9aXatBDkVQnkfMuS2HVrL8DHkomi4lrnwhvo7A0GrWpT6M2laPi90j3nrxYIs5sMxp5qFvonrU3t+9Iu1q1mbBpA5lOJ5c0bcaQFudU2MstjhACoxC8uXxJUDs9h9fLOyuWcXWrNhWu+DyQlcWG1OOk2fPJdbtYd/woFzRuEna/1JIMa9+BuXt3B3x+Aoi3WOlQO7w9mTOJcH7Da4DmQojGaIZ8KFCyfPEgcBHwrRCiFVpFT1plTrS8SO/uAmNePJvEAfYfkbYrEaaWyOznQeZQ9Px2gOpG5ryOqPZx1czLObvgnmWd6EDaJyJslyHzx1FU7FSIG9wrkb7jmk67dBekYU4s8OJDbDR5d5/iOyhiwO0XsPDHpexZvx9HnhOT2YhiVHjhp0fpNjj8Vc7WVTsZ9/h37Fq3l/gacVz3xOXEJETz5+QV2HMdqL7gxZzRZAxZWARaWGbso+PZuHgLtlgrQ+4dwI3PXOnvUfrUdw/wxZPf8/tXC3E73DQ6twEPfjKChq2C9VFKw2l3sfq3dThyHXTq346apcypIlzdqg1en8r7q5aRbrdT3RbFw917MLRtu1Kva187mfZVaLD2Z2XqHj/psOPyeSskE/DPsaMBXr/d42H8hn9Iy8+ne736WIxGLmjUpFyt8Don1+WJHr14a/lSTAYFKSXxFivjr7j6jGh/V15EOGpvQohBwIdoKYlfSylfE0K8AqyVUs4syGz5AohBs45PSinnljZmSkqKXLt2bbkmK30nCqo5jyLMXbXO9iFi5jL/S2Tu+wQbNoPWfCL6TmRqW/Rzt60otTeWa27homY9As7fwjvZ2BYlaTpq+qXg3Rn8uohBJH6LMLVDzRgJ7iWUXlhUNGZl4XZ5+G7UZFb+upaEGnHc9upQ2vYMf2m/e/0+Hj7/hQAvXygCxaDgK9Ra0VkzW6IsfLHxPd2MlPQjJxnR9lHsOQ5/rrbFZqbHFV15dkLg5quU2qap0WQkLyufed8tZt/mQzTv2JiLhvUmKjZ0ZsXmZdt4bvAbSCRSlfi8KkOfvoLhL10X9vsvD26fD5Oi/GvCcqVx4fiv2J8drAsTb7GybuS9FZrjbb9M588D+mqXUUaTlkkl4dPBl9G7YaNyjZ3jcrHu2FHiLBY61E4+o425EOJvKaVuk96w1mAFOeWzSxx7sdjPW4Hw2s1XEOn+G5lxK0Xhk2mQOxqSpofIry5QNAxCFMjFCrTcbR0DqKtweGpIz2ZwLQdfuAsXK9gu0340dQHvXoIeTtIDhiaonv3g/pOym4BYEbGBBm3Phv2s+f0frDFW+lx7HtVqhZ+G5nZ5eLLfy+zZcABnnpOjZgNPX/wqz//0KN0vDc9D/27UZNyOwNWHVCU+tVh+c4FYpNliwmA0YDQbeeq7B0KmF874+HfcDndA4Y3L4WbZjNWcOJhGzQZFm1JCCIwmI0d2H+PB857F5XDjsrtZFG3h+1emMOavN3W9brfLw/ND3sSeGxjWmvzOTJq0b0RSnWrUP6cu0XGVUwEIWgu0UOzLymT+3t0oQmFAs+bUja1a8anG1arpGvTk2JgKP3D2ZoZu5Ve8ScU9v/3C6hH3lMtTj7NY/vUN0NPBGVH6r1VX3kNg2MEL6n5k9gua0JaxEZi6FBUOmbuhr2fiRZq6owgD0nIxuOaVOM8MtmD51MD5OMC1GFQHWHroxtulZwt4tiCVeuD8RYuJ+ytCQ1HwgBFRWhFUlKY7ImJGFmzs5uF/AAmtjF8oMUjHFEIb8wJJXaUWxD4N5p5I31GkiGfMAz8yd/xivG4fBpOBL5/6gWcnPhxWw2OAOd8sYvc/+/y53F63Dy8+3rz5Y6akfukvDCqNPev3hyWRbo22ctNzV5FycQcan9ugQLBLn+1/7fI3qS6O2WLiwNbDAQa9kA/vHkduZr4/l9yZ78Lt9DD24W95adrjQeevX7hZt1LTZXfxyjXvYoux4nX7uPaJIdwy6voq9ao/XbOaMX+twidVhBC8u2Ipz5zfh5rRMeS6XfSo16DS1QXXHdPPTN518iReVa1QzLt1jRoczsku0y1RhGDx/r1c2qJ8GTX/HzgzDLp3PxBC9tP1G9K1UBPWMNSFxO81dUPPuoLWdSUr2QwIz1owt0bEj0Jm7NY0UKTUbJ+xJUTdqknyGmpqsrzF/hilew0yszBPWUKODxlzD0qMpkkipbtAh31dwTkqhauKMhE1tQ5Hlj4QNQJpn4b0bgfjOZA4AfK/BPdKUKojou8Ea6EyYCl58qIOouYchDCj2n9CnugO0o2qqjRtmoDPUwufV/F3tH/jpo+YfPzLkDnexVk4YWmQPrj2GUh2rt1Lmx4tyxyjfss6nDiYXuZ5iiJocE493ayVkjQ+twFbVuwoCtkU4HF5qNOs6OErpWTHmt1knshhw+ItupWlf/2+Dj1KpjwWR6oSe47muU97fxZ1myWHJfNbEXaeTGf0X6v8PTMLGfXnQq2xgwCfqnJbh0482bP8G8ih8OjokoD2LS+PymNxHuzWgyUH9pdZaCSpeIXq2c4ZYdBRywpTOLTfsncfMvtFRLUxSN9J9Jsz+5C+k5rfqiRA9V81+V3vPqShBTj/gPSBSGHWrjfUh2pfIww1NVGtzLuCu//kfY40n4cwd9R0z91rCd7EDAOZDr7jYD8C9vFoYSMHhVo0ovpUhPGdoMuEtT8y9zV0Hxq2wVqqpHOBlp5ZsEmsKHDBFWn4PF5GP1OUkaEYFP6Zv4kel5ftpZt1OviAZtDM1vDCVsNevJbNy7aX2nhCm69CyoAOAcfSj2aQcSyT+i3rYIspinVf9dBg5nyzKMCgm60m2vdt4682Pb7/BE9d/D8yj2eBCJYJKKRwE7UkHS5oo6unXhJnvovJ7/xSZQb9j9278Kr68ygephi/YT3n1W9ArwaNKuW+FzZqwu+7dwaoRwqgc3KdUkNDpdEqqQY/XnUdry/7k42pqUSbjOS63UEPD5+q0ruS3sfZxhkhziVMzSg9VFGIV0vnk54CGdwQFKsOFUIgzF0RUdcj1CPgmAS4tfCGdIB3DzLrQe1k14oQAzqRjqnaj47JVMiYF86/YDztYVQYn3VoOuc5r+heJQzJYL2CIE9dJPilBGTeJ5TUk7dGSfpfn4nFFmgQwtkoB7h0ZH+s0cG5vDHVosPypAHa9jyHF6Y8Rp2mtVAUgS3GSvchnTFbTUTF2YiKtRGfFMubc573FxHZcx08d+nrDG92P09c9DLX1hrBpLdm+Mes07Q27yx4iWYdG6EoApPFRL9hvXlx6uP+9/f8pW9wfG8qjjwnjtyS6Z0aJosxZAFUdHw07fqEJ6mQdSJY46WykGE2T3d4PfxUioxueXm2Vx+q26KwFWSzWI1G4iyWUitH3V4vw2dMpdnH79Pk4/dIGfcpi/YFFnK1r53MT9cMZdt9D7HmznsZ1Lylv4WcIgRWo5EnevSiRnTF0mDPds4ID10oiUhTV/CsDuNsrTAHtZQU+BAev7SPJzh326fFwn3HKWrnFnRlUcm9DE/LRGsKXVNrwiGzCalfXjRpcC8L+aqIfwNpag/547UVhPUiRMz9CKWgZFo9pnudVCGumo80h/Yw8Hp8bFyylfWLt9D76u60Pf8c3fiv1+Ol55Vd6begD3O/XYRQFAwGBaPZwP9mPl2umHG3QZ3oNqgTbqcbo9mIoig48hxsXLINi83Mub1aBcTM3751DP8s2ITH5cVTUCA04dVp1G2WTK+ruwNwTtfmjP37HdwuD0aTIaCQ6cDWw6TuT9PVblcMCiaLNocGretx59vDdOecfjSDjYu3lPneFEXQvm/V9eEc0KwFn/+9Bl8ZYQoI9NhPldoxsSwYfjs/79jGptTjNK9enatbtQlokBE01wnjAzZSM5wO7vj1Z6ZdewMdk4O7ggkheP/igaw8fIg/du/EajRyZas2VVppeaZzRhh0AFHtU2Tm/VprORSKmiQX/6MUYGqHEBaksRW4l+sPZgyRVqeG8KSEQfPYzeeFENaKQhRorGPtB44ZBKdLFvb7NBe8nzEIS2+kZxfy5DWUbdChtFi5EIrWrk6nwxEApvYFDaQDjZjXo5CZbsFsNeLzqaheHz+P/h2pqvzx1QL6Xt+TR7+422+g5//wJ18+PYGMY5nE14jn5peu5fP177JxyTbiqsfQdVCnsPp86mG2FoVwbDE2ug0KFk/KycjlbJVtrQAAIABJREFUr9n/BGidgxba+Ontn/0G3T+mzlzys+0oOsVKAPVaJHPVQ4Np2KY+bXq0DPlg2rpiB0azsVQxMoPJgDXKwm2vDg15zqnSsnoS96Z05ZM1f6EWFGbrxbejjCYuK7aJKKVk5eFDrD9+jDqxcVzStFm5NcejzWZuOrc9nKvfFKQ4W9NSdbNiAF5cPJ9fb9Da6aXl5/PVP2tZfuggdWLjuLNTCj3qNwgpRhYhkDPHoCuxiOrjkb6jWnchEQeZN4Oai+ZVW0GYEfGvaxfE3Fcgj6sTX3TNRk2domXFxD2FKFRjtPbTPNwg42oBQ2OEMCDjni+IRXu0sUWUVtFZoNEuYh5FupaBmlVsXkaIG4XwHdG0yW2DEUpB71NdJUg9TGDTb48WDiLmEa1PasAKxIxD3sPNLyaj+lQmvD4Nb4mmEIt/Wk7/4X1o17s1i39azod3f+HPGc86kc24J75n5NvDuOzeARWeW3nIzcjDYFTw6ES1SnY+CkWzjo10vXOzzczFt/Rl8MiyBacSasbrhqYURVCzYQ2sURbO7d2K65+8gloNq9ajvL/reQxq3pK5e3ZjVBQMiuCdFcvwqipeVSXKZKJj7WR/VojT6+HmGVPZlp6Gy+vFajTyvyWLmHzN9TRNPDWpglAsO3gg5GuFfU5T8/IYPPE7cv+PvfMOj6J62/B9ZvumN3pogqAB6YIVEFEQEBVB7AVFP3vF3nvv5Qc2VBARLDQFEUFUUFBQeu8tEELq9jnfH5MsWXY22XQS5r4uLt3ZKWeT7Dtn3vO+z+Px4FNVVu/P5LftW3n2rP5c0L56FEPrG3UmoBcjTE0082ZAps5GuqaB/18wtUU4hwUd7BUlBjVlChy8HmRW0dFFtenFKRfvr8isvyF1BsLUBBFzA9I1E9RstHyzAlgRCc8EFRcV5yVIS1ek6xuQ+Qh7f7CeHtRcEaYUSPsRWTgd/Mu0G4Hz4sMB/AikzCGyJrsmFoZQwNQSEfdwxX9wSnKRymPJa6k0aNWdyx7qyY8fz8NsNoUEdNCC+tMjXsNi0xpvjmzz9xR6+OzJrysd0LP3HWLjsi2kNk2mVcdQTfJAIMD09+cw/f05eAo9QUegkI9nUuh6dundk8XYHDZGv3QF79z2UUgXalxyLEP+79yoztHh9PbEJcXiLvCELKhabBaenvZAlbX+67ErL5e3/lzEb9u3keJ0Mrprdwa1bcdN3U8O7tO7RSumrF5FttvF2a2Oo0/LVpiK0k5j/17KyszMYGVMgc9Hoc/H7T/OZKaO8XRV0KVRZKP1RrGa0Nw7SxaT6/EEnX2KlRGfWDCPQW3bYangYuuxRJ0L6CURSgwi5lI0bbBwFEsGNFykdZiqWZA1nNAFSwnSjSz4CBH/KEJJgtQZyMLJWr7a1AThvAphCS2/E5a2CMv9h88i3UjXXAjsBktHsPZCiRkBRNEx6J5byptmzVzafBxYuleqllnmPqMjOeBHHroNGvyJ2WqOeP6yFvVyD+Ti9/kjVoSUOi4p+d8945n2/hwsNjMBv0rz9k157oeHSEzT8v8vXPEWi6b/HbyZmMymkO5Rk9mEI87OlY+VbtlXkj++X4KimFBLlPvlH8xn98a9pS7oqqoaNLh4ae5jPDb0RfZtO4DJpCAUwd3jbipXMFelZPr6tXy58j/8gQAXtj+R4RkdI1aK7MvPZ8jEz8nzeghIyZ78PMb8NJvv164h2+PGLASXZJzE0PYnMOY0/QXdqWtWhZU5SmBT9kH2FxRUy4Jjj6bNSHE4yHKFT14eOaMPAAu3bdW1afOrKttyDtGmmp4e6hN1OqCXhVTztPZ/94yixUq9BU0/uGejBnZq+XfnpSixo9D0xqK4hn8LMutSNBs7j9aFam4HyeMR0djWmUrRDhExweYiQFNiVA9oLf9KOTsQPfq62shDyMBueg3uxhs3/a985ywiuXFShYI5wNwvfmXmuLn4PL5gbffm/7bx7KVv8PLcx9m+dhd/TFsaIqIV8Aew2i00bdsYqUpO6pPBJWOGRq2jsm/bfv5bsBq/LzSoeT0+vnr5ex6eeKfucf8uWMVzl75BYb7mb5qWnsITU+8FIXDluTiuc8uomqlKct9PP/Ljxg24ihYs1xzYz4wN65hw0Qjd9vRx/yyhwOcNKRd0B/z8vHVz8PWKzEzmbd3M2wMH616ztMqYaKtmKsLsy69hxJRJbC7SgbEqJh4+sze9izo4U5xOtueGTx78qlrqYqvBYeptQJcygDw4EvzbOLyAGgH1gLZg6PkDWfAppExBmCNbkYVc59DdILMJ3ixkIfhWI/PHIeJuizA2L7LgM3BNQVs8taC7KBpz/eFj3HORuU8ULdxKpH0AIv6pcgT2UqogZAGxiU15ZNLdPDPyNRRFazSKxn3I5rRy3XMRFmKj4Js3ZoRpqgf8AVb9vo7szBzW/bVR13PU6/bR6qQWPPj57eW+Zub2A1hslrDPJ1XJrg361UAHdh/kkcHPh4x11/rd3NP3CSZu/6BCC8FrD+znh43rQ1QLXX4/KzL3sWDbFvq2bB12zOJdOyM29Rw+h495WzaxMnMfHRqEyyNc0O5Exv2zJKQ5RwCtEpNoEFM9NoUAyU4nc6+6Dq/fT6HfFxakb+jag3vmzAppLLIoCr2apleZiXJ9p07UoVcIz69aCqSsYA4c1nPxgMxD5j0f1SVkIAv8Gwif+XvApS+AJaXUOk3z34LA5hI18UfMxqz9UWK1jlTp/Ve7caiZ2rnxgns2Mie8JT0iokGkNxBFTwm9Bnfj/b9f4vSLetLtnE6YIxg+l6THgC6VaprJO6jvKamYBAU5haQ2S0YvE2SxmUu1iiuNFhnNdDs9zVYTJ52pXwH102fzg+bPxUgJPrcv6BlaXv7ctUN3YbXQ5+P3CIuI6fHx0XVkqCp/7tqp+95N3U/m+JRUYoqqWpwWC/E2G28MqPiie3mwms26M+4Bbdpyc4+e2M1m4qxWbCYT3Ro35c0aGld9oM7P0KX0aybJSmKo8qJ/7RELgCUpDlR6HXaqJqIV3dXL/55vOXiXEWYSLZzgHAVKKtjPQTEdzhfKgv8R3qzkAc+vyMA+hCmKwBb/EOTcRehnFmAfHJzl//LV77xy3XsItFxxIKCimJSwQFaS3775k3H3f8Hol64seww6NDu+Mfu2hfcFBHwBmhzXkCbHNSQxLQFPoTdkHCazifOu71eha8YnxzH01oFMe292MC+vKAJ7jJ2L7x6ie8yBXQd1n1gC/oDWbVrE3C9+ZcIzU8nafZDjOrfkhpeu5MRe+k5ByQ4nZkUJa2O3KgrL9+5h9Izv6N2iFRe1PzFYUnhD1x5RtcdbTCaSI6QpnBYL315yOQu2beHfvXtpEhfHoLbtiCmH2FV1cUuPXlx1UhfWHzxAA2cs6QkVs547VqnTM3S14DNkZk/k/n7IzB6oea8RNEkyNdcErI5EOCHmZkh4Gy3VoYPecXq7mVLB3IrwLlYbOCIYJfj+RTe9IgtBFqLEXBoSzIGiWbzODUJYNamAkqeRMvgzkJ5FqAevRM3srUn2Oq9Fq/RRtH+2gYgE7WkkNyuPV659D6/Li8flxefxF1VvSMwWk25HaDFTX58R8b2yiFRqKIGcA3koisIrvzxB+55tsNjM2BxWGjRP5blZD1dKe/yGF6/gljevpfkJTUlskEDvS07lvaUvRnQY6tynA45Y/TWRjNO0csBv3prJGzeNZef63bjy3az8bS1jzn6KdUv0Nej7tz4uWHlSEq+qsnzvHuZu3sRzC+czZNLn5Hm0G0/Xxk146ewBJNkdOMwWLIqim2s3CcE5pTj9KELQt2Vr7ux1KiMyOh4VwbyYOJuNbo2bGsG8AtTZGbpa+C3kvUqw5E8CBeORwoKIvQ3s/SHveUK9OxVt4bLgQ61ZiACECXjZwBl9tYRIeA158DI002dXkVJiS0TMjcF9pFqALJwEntmlm09EupFYukWWzy3yHpXShcx9oaipyYM0tYTALoIpJ09x4Lei/Txsmn66fyNYTuTPmf/oOgNJCefdcDbD7hrM1W311wTUgIrH5cHmKL+llyvPrbvdYjVTkFNIUoMEGqSn8uZvz5K97xAel5eGLdI0s+d9h8jel0PTto10r73p3638M3cFcUkxnHpBD/6atYwfP56Hqqqcc3Vfzrm6DwNHRTfLP3VoD9LbN2Xrqh3BBVp7jI1eg7vR+qQW+H1+Pnt8sm5Z58ePfMmLsx8NO6fdbGHChcMZPeM7cjweBFq6RXL4L9bl97MrN5dP//2H207W/GAHHd+OAW3asjM3lwS7jTX793PrDzPwFlWuxFptfDB4aLUH6R05OYz7Zwn/7dvL8SmpjO7Wo8xKlN93bOOTZf9w0FVIv9bHceVJXeqlFVxtUWcDOgXvEl6/7YKCj5Ext2jpl5TJyJyHwFssGWBBS124Skx4tVpzhFULkLZTELGRHeiPRFjaQtov4P4BGdiNsJwEtjODdetSurRO0MBOytR40e1CLZbPnVEkClb8VXdAzLVBLXiZfXOoKFjgSKOA4g/sOfxf6UEeuheRNgtVVSNWOJitZpoc16jU9EvJLs/y0HNwV2b+76cwoSuT2cQnD0/EEedgwHVn0bZrK9Ys3oC70INE8sFd41ny43IsNjNqQOXqJy8JpkuklLw66j3mT/6DgF/FbDHx+o3/04ybi9ImG/7ezMKpi3hm+oOHJZelZO1fG9m5fjctM9Jp27V1yHheW/Ak37/zIz9PWIjZambITefQ/2pt/eBQZg5+HclegM3/bo34+TMaNOS3a0ezen8m67MO8Ogvcyk8Ip3iCQSYtWF9MKCD5vHZIlHruTglvTl/XX8TK/dnYhaCE9IaVLuBw7qsA1w8+Us8AT9+VWXV/kxmbljHpxcMo0cT/cqtD/9ZyuuLfw+mi9YcOMDXq1cyfeSVxBlBvUqouwE9EEGrRbrRgpZDaxZK/lRTSfSt0zpLwxahVLB0RsRcAea2iKIZb3kQSiw4h+suVsnCb4oWZ6MQ7BLhwVJKiTA1hZSpWgmm9y+tSSjmeoTjQm0f/ybw/h3dNY4ksB0Z2M/J53VFvXlc2NtWu5U+l2jeJX0uOZV5E8P1ZLr2Pymkhn3jsi0s+PoPhKLQZ8SptD4pcsXQ5Q8PY+HUP8nPzsfr9mkVLVLidfv4dcpihBBF15RYbBaklLgLPCiKIOBXg4ub4x//isatG3LaBSfz27d/seDrRSV02rUA4i1xM3IXePhvwWr+W7CaTn0yKMgp4P5znmHb6h0IIVBVSbsex/HszIewO7VgY3PYGHHfUEbo+I7Gp8RF/IyNWpW+xiGEIKNBQ2Ks1oh+U2UFPJOi0Klh9fjg6vHswvkU+EqUkkqJy+/n0V/m8uPl14Ttn+fx8Oqi30Pq3z0BP5n5BUxc8S83lmiKKi8Lt23l3SV/sisvl+5NmnJ7z1NolZhU4fPVZepuDt0SSWvbgTx4FWr2TUWt7iCEDYGHyFooPoR9QIWCeZl4fiFyJ2hJHAjb4cd/tfA71Mwzkfvaaf/1/ouS9BZKw8UoabMQjiHgmoKadTny0J1EpbeuiwRhIqlBAre+Mwqr3aIJZJkUbA4rg2/qzwk92wIwZvytdOnXMWTJoH3Ptlzz1EimvDadOePnM3bM59x5+iNMful7vnrxO24/5SEmPDMl4tWTGiYybsWrXP7IMDr1yaBz3w6YbZZgoJZSFtWo+ynMdeHKcwft3kriLvAw6QVNcXHOp7+ElULq4S7wsHz+SgDeveMTNv+7FXeBB1e+G0+hhzWLNzB2zGdsWbGNg3v1PTSLsdqtnH/LudicoYHX5rRy1ePRpfBaJibRMiExbHbtMFu4+qQuUZ2jpli6+0ifeI2NBw/i0VmwXZm5D6tO+emRNfTl5etVK7hp5vf8tXsnu/JymbF+Led/+UWp7kf1mahm6EKIAcCbaBHxQynlC0e8/zrQt+ilE2ggpYzey6wCiLgxyIOjCK0WEYCnaOERpHcRMvYOlJjrwJwBUq+qxQa2srU7KowpjYhWd8UIB1hPR7pmIgs+AhEP7ukEZ9zqXsh9FBVQnEORUkVm31BULRPNzaK08aUHZQkGXtePzn07sGDyInweH6ec3502nQ93TZpMJl766TGy9x1i5/o9NGyZxocPfMF9/Z4k4A9gMilH6JpLPC4vE5//lt6XnEaztvqmxfHJcVz20DAue2gYr456L6SJqDxk7dGCbrFZR1lYHVYSUuKRUvLLpN/DUiY+j4/p781h7ucL8Xv9dD6rAw9PvIOYhNBOSleBG5/bx6jnL8dsNfPdWz/g9fhITI1n9KtX0WNA9MF47JALuGzqZA4UFiDRbmgjO3TkvLb6lTK1RZzVFlI/X4xFMem26Cc5HPh19HMEkOqoWI25X1V57rcFIRU/2pOCjzcW/8FbERqr6jNlBnShJYPfBfoDO4ElQohpRT6iAEgp7yqx/21AtU8nhLUHJI9H5r8GvnUg7KBmEVJBIl2Q9zrSMRyhxCHjHoS85zgsg2sHU+OQbswqH6fzck0fJuTGo2jlidazQARANILCcQQFv/RckXFD/utIx/maiqRvOaUHc4H26xWaOJh0o3tTUXOQUg1q0TRu1ZCR95duwZfUMJGkhonMm7iQRdOWBhcCIxXSqQGVRdOWMvwe/ZLAksQkxpRZKqmHYlLo3FeTqe145oks+XF5mcdIVTJ7/C9MfWNGxPw3ENRLXz5vBc9e9ibPzXwI0CqDXr7uPZbO1q7VuFUD7v34Zq5+8hLc+W6c8c5yyzUEVEmBz4snEECiTQWyCyt5064GrunUhXeWLA4JpjaTiREZGbr5+3YpqaTHx7Mp+2BIl6vdbObaLuGqmtGwNz8Pr45zkSolSyI8QdR3okm5nAxslFJullJ6gUlAhJo8QBNW+bIqBlcWwtoFJflzlIZ/ac5CeuWAwgI+TdhfiRmJSB4P9vPAcjLE3Y1IiWQyXUVjtHSA+Ce16hcRCzi0KpjkiSiJTyHinwDXpxw2tYCI6RN1N3Jfe2T2TUXVMmFXA2yAGaynIlKnIRosQqR8q+Xd9ZAFpWrHe1we5n35G1Nfn8GaPzeENML88NG8qFIbiiKialICOPfavlisFVjakZLh92qm2tvXRv4y25w2HHF2bE4rQsDGf7awd0sp2vkl8Hn8LJ+3kqw92Ugpuf+cp1n64zL8Xj9+r58d63Zz/7nPkLXrIDEJMRXS3rn46y/JdruDfwEq8N36NXz+X9k3qJpkdLcenN/uBKwmE3FWGzaTib4tW/PQ6X109xdC8PHQi2ibnILDbCbWasVhNvPQGX0iLqKWRYLNHnJzKEnD2Or7Th/NRPPNaQqUtP/ZCfTU21EI0QJoBcyr/NDKiZKK/sw2AMrh7I+wdkFYazYfqTgvRDoGgm8ViDht8bX4y+5fS6npmDAkkbXTJcTdhxJzhGKeEocUiYCed6dadKMJZ8vK7dzb93F8Xj8+jx+zxUSnPhk8+e0YTGYTgXL4Op4xTPdPJoxWHZrzf29cw3t3fIrZasLvC0SVgjFZTPw6ZRGtOjRnfwSPUme8g2ueuoQWGc15dMjzuo1CxU8HQuisn6NV/BzKzOHAzix2rt8dVp3j9/qZ9v5srn9eM8YoyC1k9ie/sGLhGtLbNWHwTedErJ9fvT+TLJfejRreXbKYqzpV/O9WSsmyvXvYnpNDRloD2qZUTujKpCg83+8c7j7lNLZkZ5Men0DjuMgLwwBN4uKZdfnVbMjK4pDHRUZaQ5zl1GAvSZzNxnltj+eHDetDmrMcZjM3R7HImu/1okpZr8omq7rKZSQwRUrdZDVCiNHAaIDmzatWsF7EXIX0/EJ4aqNJZEOLKJHSW5SXt4KlYzA9Ua7xCTtYu+m84Sylo7UiRLg5xFwHuU9zpB46tj66TyhSSp66+BVys/KD2/xeP8t/WcmM/81h6C0DOeeqPmxYuhl3Yfgs3ea0BTtOb3//hogNO3oMuqE/vYefyvJfVrJm8Xqmvz8HV75+vXoxPo+fnycs5OonLqH7OZ1Y8+eGsBuB3+un76Wns3Pdbiw2fXOK5EaJdDzjBPZt38+6vzaF5eOllKS3a8Ki6X+HuCCVvMaOtbsBOLg3m5u730/+oUI8hR4sVjPfvjWLDq8MYbE9nzirlWs6d+XC9poR+caDWWHnKybHU1FbQ8h2ubji26/ZlnMIgZZnPjW9Oe+dd36F/T+LSXPGkOYsnzpjZW8mJXnurP6oquTHTRuwKAoguOeU0zjnuLYRj9mVl8s9s3/gn73a7ykjrQGvnjOQ1kkRnmLrENEE9F1AST3QZkXb9BgJ3BLpRFLKscBYgO7du1eprJuwdivKkb9Q5DAUAHMzRNK4ysnOuucic8YUvwIRA0n/Q1gyqmTcEhuRK1QsHG5+isbRyIaI0JwkHMO08sbCL4pq7r1g7YpIeEF3/92b9rJ/Z3iA8RR6+fzJr5k38TdaZKTTtlsrNizbijvfjdVuQVEU7vn4ZgoOFSCE4JTzu5PUsPzr47GJMZx+YU8yTm3Hd2//ENUxPo+fqa/PoEGLNOKSY8jdr+Iryo3bnFa6nn0Sz13+Ju58T7CksSRCwAm9juehiXeyf2cWN3a+l8JcVzCo2502rn/+Mqx2K8d1bolfZ/HV5rDS4XStc/STR77kUGZu8Hif14/P62fREzPZ/qDm8vPoL3P5b99enujTj66Nm0b8bE3LmP2Wxv0/z2bjwawQUa8/tm/n/SV/ckevUyt83qMBu9nCGwMGkeN2k+UqpGlcPDZz5LDmDQS4ePKXHCgsCKZr/tu3l4u//pJfr7mB2KOoY7YiRBPQlwBthRCt0AL5SCBMXk8I0R5IAhZV6QjLgRJzKdIxFPyrNINkS+S7dCSkbx0y92Xw/QMiAeReQjpJZQEy62po+DtCVP5RTahZSByAzqO2qRVK2gzUQ/cVVb1EkZqx61fsCCEQ8fcjY0drgmJKI4Q58lOSlnbQvxHmHMgj50Ae65ZsxGKzcN1zl3NwbzZJaQn0vex0khpUXct2UsNErn3mUj59bBI+tw9VlQhFhJhKaJ9Pa+756MEJmG1mYhJiGDDqLJbNW0l8ciwIwbKfVwRz/kIRCCFC1gSsDisXFy3cpjVL4X/LX+HL57/hn7krSG2azCVjhgYrVpq1bUyvwd34c8bfwcoek1nBGe8Idp8umv63bsWN+YAbJd+HGmvB5ffz1aoV3NjtZJrFx5OR1oBV+0Nz+gJ49MyzKvTzc/t9zN+6JUxn3B3wM2nVijof0ItJsNtJsJctVz1vy2byvaHywxLw+gPMXL+WSzpEZ5JytFJmQJdS+oUQtwKz0aaKH0spVwkhngKWSimnFe06Epgko7WMryaE4gRrjwodK/2bkVnDCLbLy/wIe+aDZz7Yo3O3KRVzG/QDtQVspwMgYm9Eun8gVDmyONjatGgmVUh4PqIzUvAoJQmsZecXmx3fhPiUuFIXPQN+lYDfw5xP5/H+3y+Xec6KcvHdQ2h8XCO+fP4b3AUeTj6vCz+Nn4/H5cXvDaAGAqgBGaxU8Xn9eAq97Nm8j0/WvMn6vzdxd+/HQ9ryZdGNwWw2YbZZMFtM3Pbu9SFCWmnNUrj93RsijuuhCXfw9avTmf7BbNwFHnoN6sa1z15KbKKWgrA7bUSyBpElZBYsJhP/Ze6lcVwcEy8aodVVFyklWkwmHj2jD31aRjbdKA1fKdVCemWHdZFcj5vv161lZ24OXRs3oV+r4zDrpMMAduQeCkoklKTQ72NbTulGLnWBqHLoUspZwKwjtj12xOsnqm5Y0SEDe5AFn2j5bfPxiJhrEeZwDemoz5f7NNHK7UrfSkQVBHShxCBj74CCt0rk0s2aiUVMkcmGbo5dAetpCPtZ2v72s8sM5uUalxA8POkuHjj3aVS/ekR9eSiblm/T6tDN1WMRNuez+bz5f+NQ/QECfpW9WzLpPeIUep7Xlazd2Yx//CsKckKfcNSAyrKfV+J1e1k+b2WYmQVoQf2CuwZy3vVn0+S4RuUev8lsYuT9F0Qs8xx80zl88fTXIekdaYLC4xOQ9sPXUqWkYZEOeZzNxoSLRpBVWMght4vmCYmVsl6Ls9k4LimZdVmhC8UmITirVcW/K0cLq/dncunUyfjVAC6/nxiLheYJiUy+eKSulk1GWkMsJlOYpnyMxaKrHV/XqFOdolJKpHcJ0jUT1fM78sAgKJwAvmXgmoI8cCHS+3fFL1CeY6NUZIwGJXYUIuE1sHQHUwtwjNRKDk2aubDMH0t4Dj0A3sVg649wjqjSYF7Mib2O54vN73H9C1dwyZih2COoDRZ3llYUd6GHGf/7iWcvfZ0PH/yCvVsPpxzysvN586ZxeF1e/L4AUko8hR5+/XoRsUmxXHj7eQgl0hqJRFUl8SlxuqWQVruFhs0bkN6uabXcjIbfM4STz+uK1WHFEefAFmPD39BJ5uWHO5JNQtAkNi6sbT/F6eS45JSog3mex8PsTRuYu3kjLl/o38pL/QcQY7FgKzqXw2wm2eFkzKn6FnV1iTt+nEme1xOshy/w+diUfZAPlv6lu/8pzdJpm5wS/FmA1gzVKDaO/q2roVO8hqkzWi4ysBd58ErN4FkKtGqNknfZAOBC5jyGSJtZzaMxISydqvSMwt4PYQ9V/pOBvZrjkn89kRZOZdYlSCUGHJcgnCMRomp/pfEpcVxw20AAFny9iL061SaOOHuFF57zsvO5pccDZO87hLvAg9lq4vu3f+Tp6Q/QuW8Hls7+F5NFCS1eQmvbnz/pN7r260jLjHRW/rY27NyJDRKwO22cMawn7931Sdj7ikmhz8jqyyGbzCYem3wPO9btYuOyrTRsmcbehibunzsHl9+HX1Xp0KAh7w4cUqmF++nr13L/3NmYhaJ5oEvJuwOHBK3dOjZoyM9XXcfEFf+xMTuL7o2bMuyEjDoviLUvP5+dOpZ13kCA79fPcJ/nAAAgAElEQVSt4Z5TTw97TwjBhItG8Nafi/h27WpUqTLo+Pbc3evUemFCXXcC+qHbixQLy6h9DmxCSnd0fp5HYu2mdWGWiYo0d4rKOaYiSOlF5tyvGUgLaym5fC+oO7X7Wv7LSO9viKQPqmlU6Fa9AOQeyKtwymXSC9+xf2dWMP/t9wbwewO8ePXbTNz2ga6kL2hfTFNRs9L+Hfp157kH8wn4A8QkxPDwxDt58uJX8Hm065gtJu79+OagEXV1kt6uKenttAqWE4HFo25kW84hYizWSjfA7MrNZcxPs8NMn2+eNY3frxsddAZqEBPLnfVkAbQYRRER68P0dOaLcVosPHD6mTxw+pnVM7BapE6kXGRgH/hWU2YwB7R7VMWaFUT8o2idlmWhILwLKnSNaJB5b4D7Z4ot8aIS3pIu8C5CFnXFVgfmCAFbMSkVTrksnLpYt+0+72A+ezbvo/u5nVED4Z/f6rBy9hWadG3JWvmSqH4Vd6GHgD/A+3ePJ1BigVBVVcbe9zleHSu6qkZVVfKy84MVLyZFoXVScjCYu/0+ft6yidmbNgSNLKJl+vo1qFJv4VMwe5O+sUZ9Ic0Zw/HJKWFSA3azmeEndqilUdUudSKgazok0QzVBo4Lglrk5UWYWyNSpoD1zMNt+roEkIEdEd6rAlyTCMsxRIMMgLdi/pZRESEtUBnp7SPVCYtRVYnNacMZ5+DRr+7C5rBij7FhtVuw2i0Mu2sQGadqipvte+qXp6Y0TcIZ5+CvH5ZxcG82agmFRjUgyTuYz+/f6udaq4qfPl/AyKajGdH4Bi5IvoaPH5kY0mH7+45t9Bj3AXfNnsV9c36k54cf8P26NVGfP9/rCytJBAhIlUJf9d+sapu3Bg4m2e4gxmLFophwWix0adSYUV10mviOAepGysWUTqmu9SKuqFHmFET8w5W6lLC0QyR/CIB68AaIOBOvxnuhrk5LFAgLmCKZQVeeSIJZakCiBtQKpVyG3jKAD+4ZH1JSqJgU2nRuSUpjTdO656BuTNzxAb9/+xfuAg89BnZm8fS/Gd74evKy8mjcuhFWuwWf1x+sT7c5rdz61iiEEGxbvVNXPsCV72brquq7MS+e8Tdv/t/YEF32b96YhQxIRj1/ObkeDzdO/55Cf2jgfXDuHLo0akzzhLIbsvq2asUny/8O8xhVhKB3i5ZV9lmOVlomJrHw2hv4ectm9uTn0alhI7o1blKpNYm6TN2YoasHiJhuURogEt9GpM5CSR4bsVOyQphPJNwvFMCCMEc2bag0lo4VOEigtfJXrAElGjJO09egb92pRYWrRM67oR9nXNwLq92CI9aOI85Oo5ZpPPLV3SH7xSfHMXBUPy68/Tx+Gr+ATx/7ikP7cgj4VXau11q4O/ftQOPWDeh+bidemP0opwzpDkDz9k2xOsJL2ByxdlqcELk7s7J89sRXYR2pnkIP373zAz6vj7mbN+r+eQWkyndro5uld23UhAFtjg9qogg0/fTLO3Sq9lb2XI+bj5Yt5Y4fZ/LuksUcKKzgRKSS2Mxmzmt7PKO6dKN7k6bHbDCHOjJDl/7dRFYgPISwVc9ij3BejCz8mDAnIOEAW59quSaAiH8cefCKIp9SP9qvyQzmDhBYp4mN2c4B10yQOYAEUxNE0jtV0r0aiZtfv4Y7Tn8Ur9tLwBdAMSlY7RZuf/f6Cp9TURTu//RWrnhkGOv+2khK02Q6nnGCrk4KaCWOU9+YGebd6XX7sFjNfLbx3bBjeg7qSmJaAl6X73Ae26wQkxjD6cN6VXjsZbF3237d7WpAJT+7QBOH0tEI96sq+d7oculCCF7pP4Ahx7fn+3VrMCuCi9pncEp61WolHcmuvFwumDSBAp8Xt9+PzWRi7N9L+Xr4SI5Pqbh5t0HlqBMBHaU0HYvq014Q5nRkwmuQO4agkqNwIJLGVmvgFJYOkDJNM7vwrQbLiYiYUWGt+jJuTJF3qLnUNv6qolXHFoz99xWmvDaddUs20fqk5gy/53yaHd+k0udu2qYxTdvoG2CU5MCugygR6s63rNiuu91kNvHmH8/yzm0f8cf3S5CqpOfgrtz29iistoqr/ZXFcZ1asnzeyrDtVoeV+NQ4etta8fxv4Sk9h8XC2a3bRH0dIQR9WraqcDdpRXhu4XyyXS7UoomWJxDAGwjw0M9zmDIiTBnEoIaoEwFdmFsgiQEKwt8s0a0ppR88PyHdv4CSgnAOr1TnKIDi6I+0/6m5AwkbWE6qkNpieRHm5oiEJ0vfRwio5OcrL41aNuDWt0bV6DVLktIkKWIuv0VGuK62K9+F2WomqUECj351d1C7pSYey6979jLu6/dESNrF5rRx7TOXYjKZaJGYyLWduzL+32W4/X4kWkld35at6dGk+lJBVcH8rVuCwbwYCSzftxdvIFBpFUeDilE3ArowIxOehZwxHG7NV4AERJxmliSlF3nwKk1fXBYCZmThBGTC8yiOQZW8vhVs0el5G1Qvjhg7598ygGnvzQ5Ju9icVq58fETw9ao/1vH66A/YuX43iqLQ+5JTuf3d63HEVuEaSxmc0LMtL/30GB8+MIFN/24ltWkyVz0+gt4jDqcIx5x2Jr1btGLK6pV4VZXz27Wnb8vWR30e2Goyhy3Egtb5qudYZFAziNrS0urevbtcunRpuY6Rvv+QBR+DfxfYTkE4r0aYNG1lteCrInu5I3RPhBPRYHHFGo0Mys2h/Tm8f9enWjmgEJx5cS9uevVq4lNKl3/1+/zs27afhNT4oLhVJFRVZdIL3zLl1enkZRfQ/MRm3PLGtXQ9W1PK27lhD//X9b4QYTGLzUKH09vz0k+PRTqtQTl4buECPv9vWYixhEVRGNDmeN4cULkJlEHpCCH+llJ2132vLgX00lCzrgLf4vA3RCwi8T2ErfoWvww0fF4f151wJ/t3ZAUXH80WE41aNeDDla9HrISZOe4nxo35gkCR+NbpF/Xk7nE3YY9Qo14SKWXYbPbtWz9k5ti5YdK1NoeVD5a9XCU5/2Mdt9/H9dO+Y9ne3ShC69hslZjEFxcOj0rG1qDilBbQ60TKJSqUSM7hskqFtKSU4J6BLPwC1AKwn6upPFajL2ld4Y/vl5KzPzckkPp9AbL2ZPPnrH849fxwWeO/fljG+3d9GpJn/v3bP5FS8vDEO8u8pl5qYuuqHfo65FYzezbvMwJ6FWA3W/jiouGsytzHuqwDtEpMonOjxkd9qqi+U28CunCORHoXhUvNitgK1nXrI3OfAtc3BFM7BduQ7pmQ+l2dT+tIKfnrh2X88OHPeNxe+l12Bn1HnlZmjXkgECBz2wHWL92kaxXnKfSybdVO3YA+8blvwmq1vW4fv3/7F3nZ+cQllf9GeeIpx7Nm8fqgbksxPo+Plh2qvxroWCKjQUMy6oHsbH2h3gR0rL3BcTkUfq5Z0BU12mgWdFVTlSIDu8A1hdC6dA8E9oBrOjiHV8l1aov/3TuemWPnBnPPKxeuYe4Xv/LcrIci1oUv/OZP3vy/sbgLPPi9/qDJcklsTivN2unPig9EEPwyWUzk7M+tUEC/4LbzmPHBT5rcbonO0TMuPoW0ZlXnZ2lgcLRRNzpFi5BqPrJwMjL/baRnIbKEKJEQAiV+DCJtNiL+CUTCa4gGvyEs7atuAN5l6N8DXUjPr1V3nVpg96a9TH9/TshCorvAw6o/1rF09r+6x6xbuokXr3qLnP25eIpEsI4M5iazifjkOE4Zoq+tkXF6e11hL0URNGyZVqHPktI4iXf+ep5Th/bAGecgpUkyVzxyMfd++H8VOp+BQV2hzszQpW+1pocu/YALhBPM7SD5s5AmH2FqAg59B5lKo6QF+4tCMYOpbudll/28AqEzC3fnu1k8YyknD+wS9t6U16bjdYULQCmKAKHdZHsN7sbt712P2aL/p3bV48NZPH0p7gJP8GZgc9oY9fzlWKwVb/pp2qYxT0y9r8LHGxjURaIK6EKIAcCbaJ6iH0opw6zihRAjgCfQwt2/UsoqaxeTUiIP3VkkJVu8sRB8a5AFnyBib6qqS5WOtQeIxKI8fcmZqBnhvLRmxlBNxCbFopjCF7TMFlPEksO9m/ehVyXliLXz5Hdj6HjmiRFTNcU0bdOY95a+yGdPfM2KhatJa5bCpQ9eRK/Bx6ZanoFBZSgzoAtNi/ZdoD+wE1gihJgmpVxdYp+2wIPAaVLKbCFE1Ur+BXZpeeow3OD6DmoooAuhQPJnyEM3a05CQgGsiIQXEeaWNTKGSKiqCvkvg+srkD6wdIKEl1DM0T059BzUVTf4mswmzr2mr+4xnfpmsGn5VnxH6Jn7vH5ad2pZZjAvpmmbxjz4xe1R7WtgYBCZaL5xJwMbpZSbpZReYBIw9Ih9bgDelVJmA0gpM6lKhCB0RlwCXXH/6kOY01FSpyNSZyCSJyEa/IGw6we8GuXgJVD4UZG7kQd8f8GBs1H9+gJRR2J32nj+x0dISI3HGefAGe/AHmNjzPhbadxav4ph2J2DccQ7QlyF7DE2ht01uEKLmQYGBpUjmpRLU6CkaPRO4Mg++OMBhBC/o6VlnpBS/lglIwRQSpllmmqnDK0mxLCiRfWtBb/ewqUfcp+G5LeiOs8JPdvy1e6xrF60Hp/HR8Zp7bA5Ijf3JDVM5IN/Xubzp75m6ezlJKTGcfHd53PWZeFejgYGBtVPVS2KmoG2QB+gGfCrEKKjlPJQyZ2EEKOB0QDNm5cjIAZ2oa9LDqj6CnvHFO5Zkd/z/VmuU5nMJjqecULU+6c1S+HusTW0hmFgYFAq0aRcdgHpJV43K9pWkp3ANCmlT0q5BViPFuBDkFKOlVJ2l1J2T0srR0maEKUMtU5VXlYPpT2lKBU3OZBS8s2bMxmZfiODnJdx15mPsm7ppjKPyzmQy0+fLeCnzxeQezCvzP0Njg6klOzJyyPb5Sp7Z4OjkjK1XIQQZrQA3Q8tkC8BLpNSriqxzwDgUinl1UKIVGAZ0FlKqd81Qvm1XNT9AyCw+Yitdoi9FSV2dNTnqY+oqgqZHdC16Ut4E8UxsELnHXf/53z/bqiqod1p4+0/n6dlRrruMXM+m8+bN40NdpeqAZV7Pr6ZvpecVqExlAcpJT6PD4vNYrSgl5Mlu3dy75wfySzIR5XQtXFj3hwwiAYxxlrI0UZpWi5lTm+llH7gVmA2sAaYLKVcJYR4SghxftFus4EsIcRq4BfgvtKCeUUQiW+CSACc2rCFEywdETHXVOVl6iSKokDyBOCIfLfjmgoH88I8F9+9/aOOM5CXCc9M0T0mc/t+3rxpLF63D1e+G1e+G4/LyyvXvkfWnuwKjSNafp64kEvTb2RI7BVclHotX738vW5JpUE4u/Jyuea7b9iRm4MnEMCnBli6exeXTZ1s/AzrGFHl0KWUs4BZR2x7rMT/S+Duon/VgrC0g7QF4PkRAvvA0gWsPY2ZWBGKtQs0WoHqWQxqJtjORokoWFY2e7dkYraa8B4hzaKqkg3/HPmkpLFg8qJgq30IAhZOXcwFt1bs5lIWf3y/hNdHfxDUhMnPLuCLp74GKblkTDU1mdUjvlzxL341tFosICX7CvJZsnsXJzcNNw4xODqpM52iAEJxguOi2h7GUY1SRTLBaekpYeJWoC1nND9B/wvu9fjw66gcBvwBfO7wjtKq4pNHvwwT+HIXePjy+W+5+J4hmAz3nFLZmnMIn6pvwr4nv2bXQLyBALM3bWDxzu00jUvg4hMzjLRPOTBWFA10iUuKpf+VZ2Jzhnq2Wh1WLn94mO4xPc/rCjqP6Kpf5eRBXatlnAB7t+rX2nsKPbh11B8NQunVNB2HOXxu51clHWtQSTHf62XopC948Oc5fLlyBW//tYizPvuYJbt31tgY6jpGQD9KkNKHdP+CLJyC9G+t7eEAcNu71zP0lgHYY2wIRdDs+MY8+e0Y2vXQNzDO2pONoiO1azIrZO2uvhx68/b6fQoxCU4ccYe18PMPFTBv4kLmfvEruVlG9U0xF7Y/kWSHE0uJzl6H2Uz/1sfROqniVVLlZdw/S9h6KJtCn/Y05wkEKPT5uPPHWUYuP0rqVMqlviL9G4uEx9xFna8q0nE+Iv6ZWl0jMFvM3PDilYx6/nL8vgBWW+liWev+2kjAF/7orqqS9Us20rVf1enSl2TU81fw2Pkv4HEdYcb87KVB+YFfpyzixavfwVSk7BjwB7jjg9Gcc1WfahlTXSLGauX7kZfz9l+Lmb1pAw6zhStP6syVJ3Wu0XHMWL8uxNKumENuF1tzDtEqMalGx1MXMQJ6LSOlRGb/H6gHCZFxdM0Aay9wDKm1sRWjKApWW9kPc2npKdhjbCESvKBZv6Wlp1bX8OjaryNPfjeGcWO+YMe6XaQ2S+HqJ0Zw1mVnAJC97xAvXv0OXldonv3Nm8bSqXcGDVtUTKa3PpHscPJ477N4vPdZtTYGa4S1DlXKiO8ZhGKkXGqbwCYIZBKuyetCFk6sjRFVmN4jTsVsNVPyoUIIgcVu4fSLTq7Wa3fr34kPlr3MzMKJjF//djCYAyyc+qdun7GqShZM/qNax2UQPYOPb6e7PcXhpGlcfA2Ppm5iBPTaRnqKVBv13qtbC3rOOAevLXiKFhnNsdgsWGwWWp/Ugtd/fbpUTZjqxuv2as1XR6D6A3jcXp0jDnNofw5//bCMjcu2GHncamZ3Xp7ujTfX68Gnk4oxCMdIudQ25naAXm7aDvbBNT2aStOqQ3PG/fcqWXuyEQKSG9V+3rPnoK588uiksO0Wu4VTBus23CGl5OOHJ/LNGzOx2CwE/AEatWrIC7MfIaVx7X+m+sjv27eFe8egpVy25RyiTbJhH1gWxgy9lhHCjEh8BXBwOLA7wXwcIubyWhxZ5UhpnHRUBHOA9HZNGXbnIGxOrVpHCIHdaWPgqH606dJK95iFUxfz3ds/4HX7KMgpxF3gYfuanTw57OUaHv2xQ4pTvxHOr6ok2h267xmEYszQjwKE7UxInYl0TQF1H8J6OtjPQYiKW7BVF+v/3sT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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -232,18 +277,47 @@
     }
    ],
    "source": [
-    "selection = df[['popularity','danceability']]\n",
-    "print(selection)\n",
     "\n",
     "from sklearn.cluster import KMeans\n",
-    "kmeans = KMeans(n_clusters = 3)\n",
-    "kmeans.fit(selection)\n",
-    "labels = kmeans.predict(selection)\n",
+    "kmeans = KMeans(n_clusters = 2)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
     "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
     "plt.xlabel('danceability')\n",
     "plt.xlabel('popularity')\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 143 out of 286 samples were correctly labeled.\nAccuracy score: 0.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ]
 }
\ No newline at end of file
diff --git a/Introduction/1-intro-to-ML/README.md b/Introduction/1-intro-to-ML/README.md
index 7129d6f3..a6821ee4 100644
--- a/Introduction/1-intro-to-ML/README.md
+++ b/Introduction/1-intro-to-ML/README.md
@@ -2,14 +2,14 @@
 
 [![ML, AI, Deep Learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?")
 
-> Click this image to watch a video discussing the difference between Machine Learning, AI, and Deep Learning.
+> 🎥 Click the image above for a video discussing the difference between Machine Learning, AI, and Deep Learning.
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 ### Introduction
 
 Describe what will be covered
 
 [![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
-> MIT's John Guttag introduces Machine Learning
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Machine Learning
 ### Preparation
 
 Before embarking on this curriculum, you need to have your computer set up and ready to run notebooks locally. Learn more about how to do this in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)
diff --git a/Introduction/2-history-of-ML/README.md b/Introduction/2-history-of-ML/README.md
index c5e2d27c..f36a4525 100644
--- a/Introduction/2-history-of-ML/README.md
+++ b/Introduction/2-history-of-ML/README.md
@@ -47,7 +47,7 @@ Eliza, an early 'chatterbot', could converse w/ people and act as a primitive 't
 "Blocks world" was an example of a micro world where blocks can be stacked and sorted and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward. 
 
 [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
-> Blocks world with SHRDLU
+> 🎥 Click the image above for a video: Blocks world with SHRDLU
 
 ## 1974 - 1980: "AI Winter"
 
@@ -80,7 +80,7 @@ This epoch saw a new era for ML and AI to be able to solve some of the problems
 Today, Machine Learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses."[source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/). It remains to be seen what the future holds but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
 
 [![The history of Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
-> Yann LeCun discusses the history of Deep Learning in this lecture
+> 🎥 Click the image above for a video: Yann LeCun discusses the history of Deep Learning in this lecture
 ## 🚀Challenge
 
 Dig into one of these historical moments and learn more about the people behind  them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
diff --git a/Introduction/3-fairness/README.md b/Introduction/3-fairness/README.md
index b69c62d3..f7beccf8 100644
--- a/Introduction/3-fairness/README.md
+++ b/Introduction/3-fairness/README.md
@@ -24,7 +24,7 @@ As a prerequisite, please take the "Responsible AI Principles" Learn Path and wa
 Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
 
 [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
-> Video: Microsoft's Approach to Responsible AI
+> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
  
 ## Unfairness in data and algorithms 
  
@@ -71,7 +71,7 @@ Stereotypical gender view was found in machine translation. When translating “
 An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
 
 [![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
-> Video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
 ### Over- or under- representation 
  
 Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.
diff --git a/README.md b/README.md
index 6f6bda32..adadad70 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 > Future space for Promo Video
 [![Promo video](screenshot.png)](https://youtube.com/watch?v=R1wrdtmBSII "Promo video")
 
-> Click the image above for a video about the project and the folks who created it!
+> 🎥 Click the image above for a video about the project and the folks who created it!
 
 ## Pedagogy
 
diff --git a/Regression/1-Tools/README.md b/Regression/1-Tools/README.md
index f8cc59d5..be2fd391 100644
--- a/Regression/1-Tools/README.md
+++ b/Regression/1-Tools/README.md
@@ -22,7 +22,7 @@ In this lesson, you will learn:
 
 [![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
 
-> Click this image to watch a video on using Python within VS Code.
+> 🎥 Click the image above for a video: using Python within VS Code.
 
 1. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well to ease the setup for some users. Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a virtual environment.