add a few additional links

README.md

@@ -2,7 +2,7 @@
 
 Course materials for [General Assembly's Data Science course](https://generalassemb.ly/education/data-science/washington-dc/) in Washington, DC (8/18/15 - 10/29/15).
 
-**Instructor:** [Kevin Markham](http://www.dataschool.io/about/)
+**Instructor:** [Kevin Markham](http://www.dataschool.io/about/) ([blog](http://www.dataschool.io/), [email newsletter](http://www.dataschool.io/subscribe/), [YouTube channel](https://www.youtube.com/user/dataschool))
 
 [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/justmarkham/DAT8)
 
@@ -43,7 +43,7 @@ Tuesday | Thursday
 * [Introduction to Python](http://introtopython.org/): A series of IPython notebooks that do a great job explaining core Python concepts and data structures.
 * [Python for Informatics](http://www.pythonlearn.com/book.php): A very beginner-oriented book, with associated [slides](https://drive.google.com/folderview?id=0B7X1ycQalUnyal9yeUx3VW81VDg&usp=sharing) and [videos](https://www.youtube.com/playlist?list=PLlRFEj9H3Oj4JXIwMwN1_ss1Tk8wZShEJ).
 * [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/gist/rpmuller/5920182): Read through the Overview section for a very quick introduction to Python.
-* [Python Quick Reference Guide](https://github.com/justmarkham/python-reference/blob/master/reference.py): My beginner-oriented guide that demonstrates Python concepts through short, well-commented examples.
+* [Python 2.7 Quick Reference](https://github.com/justmarkham/python-reference/blob/master/reference.py): My beginner-oriented guide that demonstrates Python concepts through short, well-commented examples.
 * [Beginner](code/00_python_beginner_workshop.py) and [intermediate](code/00_python_intermediate_workshop.py) workshop code: Useful for review and reference.
 * [Python Tutor](http://pythontutor.com/): Allows you to visualize the execution of Python code.
 
@@ -584,7 +584,7 @@ Tuesday | Thursday
 
 **Resources:**
 * scikit-learn's [machine learning map](http://scikit-learn.org/stable/tutorial/machine_learning_map/) may help you to choose the "best" model for your task.
-* [Choosing a Machine Learning Classifier](http://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/) is a short and highly readable comparison of several classification models, [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) is a model comparison table that I created, and [Supervised learning superstitions cheat sheet](http://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html) is a more thorough comparison (with links to lots of useful resources).
+* [Choosing a Machine Learning Classifier](http://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/) is a short and highly readable comparison of several classification models, [Classifier comparison](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) is scikit-learn's visualization of classifier decision boundaries, [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) is a model comparison table that I created, and [Supervised learning superstitions cheat sheet](http://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html) is a more thorough comparison (with links to lots of useful resources).
 * [Machine Learning Done Wrong](http://ml.posthaven.com/machine-learning-done-wrong), [Machine Learning Gremlins](https://www.youtube.com/watch?v=tleeC-KlsKA) (31 minutes), [Clever Methods of Overfitting](http://hunch.net/?p=22), and [Common Pitfalls in Machine Learning](http://danielnee.com/?p=155) all offer thoughtful advice on how to avoid common mistakes in machine learning.
 * [Practical machine learning tricks from the KDD 2011 best industry paper](http://blog.david-andrzejewski.com/machine-learning/practical-machine-learning-tricks-from-the-kdd-2011-best-industry-paper/) and Andrew Ng's [Advice for applying machine learning](http://cs229.stanford.edu/materials/ML-advice.pdf) include slightly more advanced advice than the resources above.
 * [An Empirical Comparison of Supervised Learning Algorithms](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf) is a readable research paper from 2006, which was also presented as a [talk](http://videolectures.net/solomon_caruana_wslmw/) (77 minutes).

other/advice.md

@@ -18,6 +18,7 @@ There are many **online courses** to consider, and new ones being created all th
 * edX's [Introduction to Computer Science and Programming Using Python](https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x7) is apparently an excellent course if you want to get better at programming in Python.
 * Coursera recently added many other data science-related [specializations and courses](https://www.coursera.org/browse/data-science?languages=en), most of which I am not familiar with. However, [CourseTalk](https://www.coursetalk.com/) is useful for reading reviews of online courses.
 * Some additional courses are listed in the [Additional Resources](../README.md#additional-resources-1) section of the main README.
+* I will also be teaching [my own online courses](http://www.dataschool.io/learn/), which will range in level from beginner to advanced. (Subscribe to my [email newsletter](http://www.dataschool.io/subscribe/) to be notified when courses are announced.)
 
 Here is just a tiny selection of **books**:
 * [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) is my favorite book on machine learning because of the thoughtful way in which the material is presented. The Statistical Learning course linked above uses it as the course textbook, and the [related videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) are available on YouTube.