,

Module 1/.ipynb_checkpoints/Getting Familiar with Numpy-checkpoint.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -45,13 +45,13 @@
        "        [1., 1., 1.]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.matrix(np.ones((3,3)))"
+    "np.matrix(np.ones([3,3]))"
    ]
   },
   {
@@ -102,7 +102,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "X = np.matrix([5,5]).transpose()\n",
@@ -119,7 +121,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# ====================== YOUR CODE HERE ======================\n",

Module 1/Getting Familiar with Numpy.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -45,13 +45,13 @@
        "        [1., 1., 1.]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.matrix(np.ones((3,3)))"
+    "np.matrix(np.ones([3,3]))"
    ]
   },
   {
@@ -102,7 +102,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "X = np.matrix([5,5]).transpose()\n",
@@ -119,7 +121,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# ====================== YOUR CODE HERE ======================\n",

Module 1/README.md

@@ -0,0 +1,8 @@
+
+Module 1 - Anaconda Installation
+
+Anaconda is a python data science platform which helps you install and manage python packages on your systems.
+
+Downloading Anaconda will help save you the hassle of installing the libraries that this course requires manually, as it already includes most commonly used libraries.
+
+Download Link - https://www.anaconda.com/download

Module 2/Linear Regression.ipynb

@@ -115,6 +115,7 @@
     "theta = np.zeros((2,1))\n",
     "theta = np.matrix(theta)\n",
     "X = np.matrix(X)\n",
+    "y=y_init\n",
     "X[0:5]"
    ]
   },

Module 2/README.md

@@ -0,0 +1,4 @@
+Task 2
+1. Complete the implementation of linear regression.
+2. Go through the first section of https://scikit-learn.org/stable/tutorial/index.html - Introduction to machine learining with scikit learn.
+3. Refer to the blog (https://medium.com/deep-math-machine-learning-ai/chapter-1-complete-linear-regression-with-math-25b2639dde23?fbclid=IwAR1hyAw2RWw-eLcKrPKLDaVmfhXvDkY-u4hxk5tlBiqX9k4Au95yoJ9x4-w) for clarity on linear regression.

Module 3/README.md

@@ -0,0 +1,5 @@
+Task 3
+
+1. Go through week 3 of the Andrew Ng course on Coursera
+2. Use "git pull" to get the latest version of the repository
+3. Complete the implementation of Logistic Regression in module 3.