Update README.md

Author: sarincr

Diff for: README.md

@@ -3,52 +3,52 @@ Basic exercises on Machine Learning with Python
 
 ![alt text](https://github.com/sarincr/Machine-Learning-Python-Bootcamp/blob/master/IBM%20Badges.png)
 
-01.Python Basics.ipynb
-02.Arrays.ipynb
-03.Numpy.ipynb
-04.Lists.ipynb
-05.InOut.ipynb
-06.Tuple.ipynb
-07.Sets.ipynb
-08.Dictionary .ipynb
-09.Directory.ipynb
-10.Exceptions.ipynb
-11.Custom Exceptions.ipynb
-12.Class.ipynb
-13.Inheritance.ipynb
-14.Modules.ipynb
-15.Math and Stats.ipynb
-16.Matplotlib.ipynb
-17.Seaborn.ipynb
-18.SimPy.ipynb
-19.Pendulum.ipynb
-20.Arow module.ipynb
-21.Decision TreesVIz.ipynb
-22.Requests.ipynb
-23.Tensorflow Basics.ipynb
-24.Stumpy.ipynb
-25.Linear_Regression.ipynb
-26.Logistic_regression.ipynb
-27.XGBRegressor.py
-28.Ridge_Regression.ipynb
-29.Support_Vector_Regression.ipynb
-30.Lasso_Regression.ipynb
-31.Bayesian_Ridge_Regression.ipynb
-32.MLPRegressor.ipynb
-33.Random_Forest_Regression.ipynb
-34.PLSRegression.ipynb
-35.DecisionTreeRegressor.ipynb
-36.Agglomerative_Clustering.ipynb
-37.ARIMA.ipynb
-38.K_Nearest_Neighbors_Regression_.ipynb
-39.SVC_linear.ipynb
-40.SVC_RBF.ipynb
-41.SVC_Poly.ipynb
-42.MLPClassifier.ipynb
-43.KNeighborsClassifieripynb.ipynb
-44.GaussianProcessClassifier.ipynb
-45.DecisionTreeClassifier.ipynb
-46.RandomForestClassifier.ipynb
-47.AdaBoostClassifier.ipynb
-48.GaussianNB.ipynb
-49.K_Means_Clustering.ipynb
+01.Python Basics \
+02.Arrays\
+03.Numpy\
+04.Lists\
+05.InOut\
+06.Tuple\
+07.Sets\
+08.Dictionary\
+09.Directory\
+10.Exceptions\
+11.Custom Exceptions\
+12.Class\
+13.Inheritance\
+14.Modules\
+15.Math and Stats\
+16.Matplotlib\
+17.Seaborn\
+18.SimPy\
+19.Pendulum\
+20.Arow module\
+21.Decision TreesVIz\
+22.Requests\
+23.Tensorflow Basics\
+24.Stumpy\
+25.Linear_Regression\
+26.Logistic_regression\
+27.XGBRegressor\
+28.Ridge_Regression\
+29.Support_Vector_Regression\
+30.Lasso_Regression\
+31.Bayesian_Ridge_Regression\
+32.MLPRegressor\
+33.Random_Forest_Regression\
+34.PLSRegression\
+35.DecisionTreeRegressor\
+36.Agglomerative_Clustering\
+37.ARIMA\
+38.K_Nearest_Neighbors_Regression\
+39.SVC_linear\
+40.SVC_RBF\
+41.SVC_Poly\
+42.MLPClassifier\
+43.KNeighborsClassifier\
+44.GaussianProcessClassifier\
+45.DecisionTreeClassifier\
+46.RandomForestClassifier\
+47.AdaBoostClassifier\
+48.GaussianNB\
+49.K_Means_Clustering\