graphic for ai/ml/dl/ds

1-Introduction/1-intro-to-ML/README.md

@@ -42,7 +42,9 @@ The [human brain](https://www.livescience.com/29365-human-brain.html) perceives
 
 In this curriculum, we are going to cover only the core concepts of Machine Learning that a beginner must know. We cover what we call 'Classical Machine Learning', avoiding the complexities of neural networks, 'Deep Learning' - many-layered model-building - and AI, which we will discuss in a different curriculum. But to understand broader concepts of Artificial Intelligence or Deep Learning, a strong fundamental knowledge of Machine Learning is indispensable, and so we would like to offer it here.
 
-TODO: a diagram like this one: https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining
+![AI, ML, Deep Learning, Data Science](images/ai-ml-ds.png)
+
+> A diagram showing the relationships between AI, ML, Deep Learning, and Data Science. Infographic by [Jen Looper](https://twitter.com/jenlooper) inspired by [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
 The major motivation behind leveraging Machine Learning is to create automated systems that can learn hidden patterns from data to infer intelligent decisions which seem to be loosely inspired by how the human brain learns certain things based on the data it perceives from the outside world.
 

4-Classification/4-Applied/solution/recipe-detector.ipynb

@@ -14,13 +14,16 @@
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   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "name": "python3",
    "display_name": "Python 3.7.0 64-bit ('3.7')"
   },
   "metadata": {
    "interpreter": {
     "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
    }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
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  "nbformat": 4,
@@ -28,33 +31,25 @@
  "cells": [
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-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
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      "name": "stdout",
      "text": [
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-      "  Downloading onnx-1.9.0-cp37-cp37m-macosx_10_12_x86_64.whl (12.0 MB)\n",
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-      "  Downloading typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)\n",
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-      "Installing collected packages: typing-extensions, onnx, onnxconverter-common, skl2onnx\n",
-      "Successfully installed onnx-1.9.0 onnxconverter-common-1.8.1 skl2onnx-1.8.0 typing-extensions-3.10.0.0\n",
       "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
       "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
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@@ -67,24 +62,19 @@
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      "name": "stdout",
      "text": [
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-      "  Downloading flatbuffers-2.0-py2.py3-none-any.whl (26 kB)\n",
+      "Requirement already satisfied: numpy>=1.16.6 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnxruntime) (1.19.2)\n",
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-      "Installing collected packages: flatbuffers, onnxruntime\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->onnxruntime) (45.1.0)\n",
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       "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
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@@ -97,7 +87,7 @@
   },
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@@ -107,7 +97,7 @@
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+   "execution_count": 4,
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     {
@@ -140,7 +130,7 @@
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 9
+     "execution_count": 4
     }
    ],
    "source": [
@@ -150,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -183,7 +173,7 @@
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 5
     }
    ],
    "source": [
@@ -193,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -210,7 +200,7 @@
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 6
     }
    ],
    "source": [
@@ -220,15 +210,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.naive_bayes import BernoulliNB,GaussianNB\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.model_selection import cross_val_score\n",
     "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
@@ -236,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,53 +231,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "KNeighborsClassifier()"
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
       ]
      },
      "metadata": {},
-     "execution_count": 14
+     "execution_count": 9
     }
    ],
    "source": [
-    "# 5 types of model fitting\n",
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
-    "model_gaussian = GaussianNB()\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
-    "model_gaussian.fit(X_train,y_train.values.ravel())\n",
-    "model_rfst = RandomForestClassifier()\n",
-    "model_rfst.fit(X_train,y_train.values.ravel())\n",
-    "model_nba = BernoulliNB(binarize = .5)\n",
-    "model_nba.fit(X_train,y_train.values.ravel())\n",
-    "model_dt = DecisionTreeClassifier()\n",
-    "model_dt.fit(X_train,y_train.values.ravel())\n",
-    "model_kn = KNeighborsClassifier()\n",
-    "model_kn.fit(X_train,y_train.values.ravel())\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = model_kn.predict(X_test)"
+    "y_pred = model.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "              precision    recall  f1-score   support\n\n     chinese       0.59      0.74      0.66       239\n      indian       0.83      0.80      0.82       237\n    japanese       0.68      0.77      0.72       252\n      korean       0.93      0.65      0.76       220\n        thai       0.77      0.71      0.74       251\n\n    accuracy                           0.73      1199\n   macro avg       0.76      0.73      0.74      1199\nweighted avg       0.76      0.73      0.74      1199\n\n"
+      "              precision    recall  f1-score   support\n\n     chinese       0.68      0.69      0.68       249\n      indian       0.92      0.88      0.90       238\n    japanese       0.77      0.68      0.72       236\n      korean       0.84      0.79      0.82       247\n        thai       0.73      0.88      0.80       229\n\n    accuracy                           0.78      1199\n   macro avg       0.79      0.79      0.78      1199\nweighted avg       0.79      0.78      0.78      1199\n\n"
      ]
     }
    ],
@@ -301,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,9 +286,9 @@
     "from skl2onnx.common.data_types import FloatTensorType\n",
     "\n",
     "initial_type = [('float_input', FloatTensorType([None, 4]))]\n",
-    "options = {id(model_kn): {'nocl': True, 'zipmap': False}}\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
-    "onx = convert_sklearn(model_kn, initial_types=initial_type,options=options)\n",
+    "onx = convert_sklearn(model, initial_types=initial_type,options=options)\n",
-    "with open(\"./model-kn.onnx\", \"wb\") as f:\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
     "    f.write(onx.SerializeToString())\n",
     "\n",
     "\n"