diff --git a/Notebooks/Chapter 3.ipynb b/Notebooks/Chapter 3.ipynb
index bb44cff..e00f674 100644
--- a/Notebooks/Chapter 3.ipynb	
+++ b/Notebooks/Chapter 3.ipynb	
@@ -45,7 +45,7 @@
    "metadata": {},
    "source": [
     "### Load Datasets\n",
-    "Datasets available on http://www-bcf.usc.edu/~gareth/ISL/data.html"
+    "Datasets available on https://www.statlearning.com/resources-first-edition"
    ]
   },
   {
@@ -1652,7 +1652,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1666,7 +1666,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index d76bdd0..4e88d20 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # ISLR-python
-This repository contains Python code for a selection of tables, figures and LAB sections from the book <A target="_blank" href='http://www-bcf.usc.edu/%7Egareth/ISL/index.html'>'An Introduction to Statistical Learning with Applications in R'</A> by James, Witten, Hastie, Tibshirani (2013).<P>
+This repository contains Python code for a selection of tables, figures and LAB sections from the first edition of the book <A target="_blank" href='https://www.statlearning.com/'>'An Introduction to Statistical Learning with Applications in R'</A> by James, Witten, Hastie, Tibshirani (2013).<P>
 
-For **Bayesian data analysis**, take a look at <A href='https://github.com/JWarmenhoven/DBDA-python'>this repository</A>.
+ For **Bayesian data analysis** using PyMC3, take a look at <A href='https://github.com/JWarmenhoven/DBDA-python'>this repository</A>.
 
 **2018-01-15**:<BR>
 Minor updates to the repository due to changes/deprecations in several packages. The notebooks have been tested with <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/ISLR-python/blob/master/Notebooks/Python%20module%20versions.ipynb'>these package versions</A>. Thanks @lincolnfrias and @telescopeuser.
@@ -21,7 +21,7 @@ Chapter 6: I included Ridge/Lasso regression code using the new <A href='https:/
 <A href='http://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%209.ipynb'>Chapter 9 - Support Vector Machines</A><BR>
 <A href='http://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%2010.ipynb'>Chapter 10 - Unsupervised Learning</A><P>
 <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Simulate.expected.misclassification.rate.ipynb'>Extra: Misclassification rate simulation - SVM and Logistic Regression</A><P>
-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>
+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://www.edx.org/school/stanfordonline'>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:
 
  - pandas
@@ -39,7 +39,10 @@ See Hastie et al. (2009) for an advanced treatment of these topics.<P>
 
 #### 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
+https://www.statlearning.com/
+
+James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). <I>An Introduction to Statistical Learning with Applications in  R, Second Edition</I>,  Springer Science+Business Media, New York.
+https://www.statlearning.com/
 
 Hastie, T., Tibshirani, R., Friedman, J. (2009). <I>Elements of Statistical Learning</I>, Second Edition, Springer Science+Business Media, New York.
 http://statweb.stanford.edu/~tibs/ElemStatLearn/