diff --git a/README.md b/README.md
index 26031dd..63defb1 100644
--- a/README.md
+++ b/README.md
@@ -555,7 +555,7 @@ Tuesday | Thursday
 * The scikit-learn user guide for [Generalized Linear Models](http://scikit-learn.org/stable/modules/linear_model.html) explains different variations of regularization.
 * Section 6.2 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (14 pages) introduces both lasso and ridge regression. Or, watch the related videos on [ridge regression](https://www.youtube.com/watch?v=cSKzqb0EKS0&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI&index=6) (13 minutes) and [lasso regression](https://www.youtube.com/watch?v=A5I1G1MfUmA&index=7&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI) (15 minutes).
 * For more details on lasso regression, read Tibshirani's [original paper](http://statweb.stanford.edu/~tibs/lasso/lasso.pdf).
-* For a math-ier explanation of regularization, watch the last four videos (30 minutes) from week 3 of Andrew Ng's [machine learning course](https://www.coursera.org/learn/machine-learning/home/info), or read the [related lecture notes](http://www.holehouse.org/mlclass/07_Regularization.html) compiled by a student.
+* For a math-ier explanation of regularization, watch the last four videos (30 minutes) from week 3 of Andrew Ng's [machine learning course](https://www.coursera.org/learn/machine-learning/), or read the [related lecture notes](http://www.holehouse.org/mlclass/07_Regularization.html) compiled by a student.
 * This [notebook](https://github.com/luispedro/PenalizedRegression/blob/master/PenalizedRegression.ipynb) from chapter 7 of [Building Machine Learning Systems with Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python) has a nice long example of regularized linear regression.
 * There are some special considerations when using dummy encoding for categorical features with a regularized model. This [Cross Validated Q&A](https://stats.stackexchange.com/questions/69568/whether-to-rescale-indicator-binary-dummy-predictors-for-lasso) debates whether the dummy variables should be standardized (along with the rest of the features), and a comment on this [blog post](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models) recommends that the baseline level should not be dropped.