Update readme

Author: rohithpr

README.md

@@ -10,6 +10,11 @@ Machine Learning algorithms in Python
 
 ### Usage
 
+* [Cost](#cost)
+* [Feature normalization](#feature-normalization)
+* [Gradient Descent](#gradient-descent)
+* [Prediction](#prediction)
+
 The first column of the training set x must be all 1s.
 
 The following code is assumed in all the examples.
@@ -53,3 +58,56 @@ Normalized x:  [[ 1.         -1.22474487 -1.22474487]
  [ 1.          0.          0.        ]
  [ 1.          1.22474487  1.22474487]]
 ```
+
+#### Gradient Descent
+
+```python
+list_x = [[1, 1], [1, 2], [1, 3]]
+list_theta = [[0], [0]]
+list_y = [[10], [20], [30]]
+
+x = np.matrix(list_x)
+theta = np.matrix(list_theta)
+y = np.matrix(list_y)
+
+(x, mu, sigma) = pyml.normalize_features(x)
+
+alpha = 0.03
+num_iters = 2000
+
+theta = pyml.gradient_descent(x, y, theta, alpha, num_iters)
+print('Theta: ', theta)
+```
+
+```
+Theta:  [[ 20.        ]
+ [  8.16496581]]
+```
+
+#### Prediction
+
+```python
+list_x = [[1, 1], [1, 2], [1, 3]]
+list_theta = [[0], [0]]
+list_y = [[10], [20], [30]]
+
+x = np.matrix(list_x)
+theta = np.matrix(list_theta)
+y = np.matrix(list_y)
+
+(x, mu, sigma) = pyml.normalize_features(x)
+
+alpha = 0.03
+num_iters = 2000
+
+theta = pyml.gradient_descent(x, y, theta, alpha, num_iters)
+
+vals = [[1, 4], [1, 5]]
+predictions = pyml.predict(np.matrix(vals), theta, mu, sigma)
+print('Predictions: ', predictions)
+```
+
+```
+Predictions:  [[ 40.]
+ [ 50.]]
+```