Update README.md

README.md

@@ -18,22 +18,20 @@ Chapter 6: I included Ridge/Lasso regression code using the new <A href='https:/
 This great book gives a thorough introduction to the field of Statistical/Machine Learning. The book is available for download (see link below), but I think this is one of those books that is definitely worth buying. The book contains sections with applications in R based on public datasets available for download or which are part of the <A target="_blank" href="https://cran.r-project.org/web/packages/ISLR/index.html">R-package ISLR</A>. Furthermore, there is a Stanford University online course based on this book and taught by the authors (See <A target="_blank" href='https://lagunita.stanford.edu/courses/'>course catalogue</A> for current schedule).<P>
 Since Python is my language of choice for data analysis, I decided to try and do some of the calculations and plots in Jupyter Notebooks using:
 
-<UL>
-<LI>pandas
-<LI>numpy
-<LI>scipy
-<LI>scikit-learn
-<LI>python-glmnet
-<LI>statsmodels
-<LI>patsy
-<LI>matplotlib
-<LI>seaborn
-</UL>
+ - pandas
+ - numpy
+ - scipy
+ - scikit-learn
+ - python-glmnet
+ - statsmodels
+ - patsy
+ - 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)
 
-#####References:
+#### 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.
 http://www-bcf.usc.edu/~gareth/ISL/index.html