Foundation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Scalar\n",
    "2. Vector\n",
    "3. Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Plotting\n",
    "2. NumPy operation\n",
    "3. Matrix Operation\n",
    "4. Vector Operation\n",
    "5. Python List, Dictionary\n",
    "6. Dataset Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1,2,3],[4,5,6]])\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task\n",
    "1. Find Max value\n",
    "2. Find max value for each column\n",
    "3. Find max value for each row\n",
    "4. Find index of max value for each column\n",
    "5. Find index of max value for each row\n",
    "6. Transpose and dot product\n",
    "7. Reshape to 9X1\n",
    "8. Calculate Determinent\n",
    "9. Mean\n",
    "10. Mean in columns\n",
    "11. Mean in rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 30, 40, 50, 60, 70]\n",
      "[0, 0, 0, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "x=[10, 20, 30, 40, 50, 60, 70]\n",
    "y=[0, 0, 0, 1, 1, 1, 1]\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, color = 'red') \n",
    "plt.title('x vs y')\n",
    "plt.xlabel('x data')\n",
    "plt.ylabel('y data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List Task\n",
    "1. Create List\n",
    "2. Append row\n",
    "3. Append column in first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[\n",
    "    [4,5],\n",
    "    [6,8],\n",
    "    [9,9],\n",
    "    [12,22],\n",
    "    [13,18]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4, 5], [6, 8], [9, 9], [12, 22], [13, 18]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4, 5], [6, 8], [9, 9], [12, 22], [13, 18], [11, 15]]\n"
     ]
    }
   ],
   "source": [
    "X.append([11,15])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4  6  9 12 13]\n"
     ]
    }
   ],
   "source": [
    "#startIndex:stopIndex:interval\n",
    "# print(X[3:])\n",
    "# print(X[:,1])\n",
    "print(X[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "6\n",
      "9\n",
      "12\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "for x in X:\n",
    "    print(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  5]\n",
      " [ 6  8]\n",
      " [ 9  9]\n",
      " [12 22]\n",
      " [13 18]\n",
      " [11 15]]\n"
     ]
    }
   ],
   "source": [
    "X=np.array(X)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13 22]\n"
     ]
    }
   ],
   "source": [
    "m=np.max(X, axis=0)  \n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "m=np.argmax(X, axis=1)  \n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4  6  9 12 13 11]\n"
     ]
    }
   ],
   "source": [
    "#start:stop:interval\n",
    "# print(X[:,0])\n",
    "# print(X[1 : 4 ])\n",
    "# print(X[:])\n",
    "print(X[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], color='red')\n",
    "plt.xlabel('X Value')\n",
    "plt.ylabel('Y value')\n",
    "plt.title('Sample Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "4 6    \n",
    "2 3 \n",
    "5 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1 2\n",
    "3 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "4*1+6*3   4*2+6*4\n",
    "2*1+3*3   2*2+3*4\n",
    "5*1+7*3   5*2+7*4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's take some notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0471975511965979"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arccos(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.918311805232932"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rad2deg(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}