Update README.md

Author: ksator

README.md

@@ -1,3 +1,6 @@
+Please visit the [wiki](https://github.com/ksator/Machine_Learning_with_Python/wiki)  
+
+
 # Documentation structure
 
 - [Introduction to arrays using numpy](#introduction-to-arrays-using-numpy)    
@@ -20,58 +23,6 @@
 
 
 
-# visualize a dataset using seaborn 
-
-we will use this example [iris_visualization.py](iris_visualization.py)  
-
-seaborn is a python data visualization library based on matplotlib  
-
-we will load the iris dataset  
-The iris dataset consists of measurements of three types of Iris flowers: Iris Setosa, Iris Versicolor, and Iris Virginica.  
-Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.  
-We will visualize the relationship between the 4 features for each of three species of Iris  
-
-```
->>> import seaborn as sns
->>> import matplotlib.pyplot as plt
->>> # load the iris dataset
->>> iris = sns.load_dataset("iris")
->>> # return the first 10 rows
->>> iris.head(10)
-   sepal_length  sepal_width  petal_length  petal_width species
-0           5.1          3.5           1.4          0.2  setosa
-1           4.9          3.0           1.4          0.2  setosa
-2           4.7          3.2           1.3          0.2  setosa
-3           4.6          3.1           1.5          0.2  setosa
-4           5.0          3.6           1.4          0.2  setosa
-5           5.4          3.9           1.7          0.4  setosa
-6           4.6          3.4           1.4          0.3  setosa
-7           5.0          3.4           1.5          0.2  setosa
-8           4.4          2.9           1.4          0.2  setosa
-9           4.9          3.1           1.5          0.1  setosa
->>> # visualize the relationship between the 4 features for each of three species of Iris
->>> sns.pairplot(iris, hue='species', height=1.5)
-<seaborn.axisgrid.PairGrid object at 0x7fb899ed15f8>
->>> plt.show()
-```
-
-![iris.png](resources/iris.png)
-
-```
-$ ls seaborn-data/
-iris.csv
-$ head -10 seaborn-data/iris.csv
-sepal_length,sepal_width,petal_length,petal_width,species
-5.1,3.5,1.4,0.2,setosa
-4.9,3.0,1.4,0.2,setosa
-4.7,3.2,1.3,0.2,setosa
-4.6,3.1,1.5,0.2,setosa
-5.0,3.6,1.4,0.2,setosa
-5.4,3.9,1.7,0.4,setosa
-4.6,3.4,1.4,0.3,setosa
-5.0,3.4,1.5,0.2,setosa
-4.4,2.9,1.4,0.2,setosa
-```
 # manipulate dataset with pandas  
 Pandas is a python library for data manipulation