JWarmenhoven
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@@ -28,8 +28,10 @@ Since Python is my language of choice for data analysis, I decided to try and do
  - matplotlib
  - seaborn
 
-It was a good way to learn more about Machine Learning in Python by creating these notebooks. I created some of the figures/tables of the chapters and worked through some LAB sections. At certain points I realize that it may look like I tried too hard to make the output identical to the tables and R-plots in the book. But I did this to explore some details of the libraries mentioned above (mostly matplotlib and seaborn). Note that this repository is <STRONG>not a tutorial</STRONG> and that you probably should have a copy of the book to follow along. Suggestions for improvement and help with unsolved issues are welcome!<P>
-For an advanced treatment of these topics see Hastie et al. (2009)
+It was a good way to learn more about Machine Learning in Python by creating these notebooks. I created some of the figures/tables of the chapters and worked through some LAB sections. At certain points I realize that it may look like I tried too hard to make the output identical to the tables and R-plots in the book. But I did this to explore some details of the libraries mentioned above (mostly matplotlib and seaborn). Note that this repository is <STRONG>not a tutorial</STRONG> and that you probably should have a copy of the book to follow along. Suggestions for improvement and help with unsolved issues are welcome!
+See Hastie et al. (2009) for an advanced treatment of these topics.<P> 
+
+For Baysian data analysis, take a look at <A href='https://github.com/JWarmenhoven/DBDA-python'>this notebook</A>.
 
 #### References: 
 James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). <I>An Introduction to Statistical Learning with Applications in  R</I>,  Springer Science+Business Media, New York.