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README.md

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 # Introduction to machine learning with scikit-learn
 
-This repo contains the IPython/Jupyter notebooks from my scikit-learn video series, as seen on Kaggle's blog.
+This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was [featured on Kaggle's blog](http://blog.kaggle.com/author/kevin-markham/) in 2015.
 
-**Want to master scikit-learn?** I teach an online course, [Machine Learning with Text in Python](http://www.dataschool.io/learn/).
+There are 9 video tutorials totaling 4 hours, each with a corresponding Jupyter notebook. The notebook contains everything you see in the video: code, output, images, and comments.
 
-## Entire series
+There are links to each of the videos and notebooks below. Alternatively, you can [watch the entire series](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A) on YouTube, and [view all of the notebooks](http://nbviewer.jupyter.org/github/justmarkham/scikit-learn-videos/tree/master/) on nbviewer.
 
-- [Read the blog posts](http://blog.kaggle.com/author/kevin-markham/) (Kaggle's blog)
-- [Watch the entire series](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A) (YouTube playlist)
-- [View the IPython Notebooks](http://nbviewer.jupyter.org/github/justmarkham/scikit-learn-videos/tree/master/) (nbviewer)
-- [Run the IPython Notebooks online](http://mybinder.org/repo/justmarkham/scikit-learn-videos) (binder)
+[![Watch the first tutorial video](images/youtube.png)](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1 "Watch the first tutorial video")
 
-## Individual videos
+There is also a [binder](http://mybinder.org/repo/justmarkham/scikit-learn-videos) linked to this repository, which will allow you to interact with the notebooks online (without downloading them).
+
+Once you complete this video series, I recommend enrolling in my online course, [Machine Learning with Text in Python](http://www.dataschool.io/learn/), to gain a deeper understanding of scikit-learn and Natural Language Processing.
+
+## Table of Contents
 
 1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb), [blog post](http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/))
     - What is machine learning?
@@ -70,3 +71,21 @@ This repo contains the IPython/Jupyter notebooks from my scikit-learn video seri
     - How can you adjust classifier performance by changing the classification threshold?
     - What is the purpose of an ROC curve?
     - How does Area Under the Curve (AUC) differ from classification accuracy?
+
+## Bonus
+
+At the PyCon 2016 conference, I taught a 3-hour tutorial that builds upon this video series and focuses on text-based data. You can watch the [tutorial video](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10) on YouTube.
+
+Here are the topics I covered:
+
+1. Model building in scikit-learn (refresher)
+2. Representing text as numerical data
+3. Reading a text-based dataset into pandas
+4. Vectorizing our dataset
+5. Building and evaluating a model
+6. Comparing models
+7. Examining a model for further insight
+8. Practicing this workflow on another dataset
+9. Tuning the vectorizer (discussion)
+
+Visit this [GitHub repository](https://github.com/justmarkham/pycon-2016-tutorial) to access the tutorial notebooks and many other recommended resources.