README tweaks

README.md

@@ -426,6 +426,7 @@ Tuesday | Thursday
 
 **Homework:**
 * Your draft paper is due on Thursday (10/8)! Please submit a link to your project repository (with paper, code, data, and visualizations) before class.
+* Watch [Kaggle: How it Works](https://www.youtube.com/watch?v=PoD84TVdD-4) (4 minutes) for a brief overview of the Kaggle platform.
 * Download the competition files, move them to the `DAT8/data` directory, and make sure you can open the CSV files using Pandas. If you have any problems opening the files, you probably need to turn off real-time virus scanning (especially Microsoft Security Essentials).
 * **Optional:** Come up with some theories about which features might be relevant to predicting the response, and then explore the data to see if those theories appear to be true.
 * **Optional:** Watch my [project presentation video](https://www.youtube.com/watch?v=HGr1yQV3Um0) (16 minutes) for a tour of the end-to-end machine learning process for a Kaggle competition, including feature engineering. (Or, just read through the [slides](https://speakerdeck.com/justmarkham/allstate-purchase-prediction-challenge-on-kaggle).)
@@ -435,7 +436,7 @@ Tuesday | Thursday
 * This slide deck defines many of the [key NLP terms](https://github.com/ga-students/DAT_SF_9/blob/master/16_Text_Mining/DAT9_lec16_Text_Mining.pdf).
 * [Natural Language Processing with Python](http://www.nltk.org/book/) is the most popular book for going in-depth with the [Natural Language Toolkit](http://www.nltk.org/) (NLTK).
 * [A Smattering of NLP in Python](https://github.com/charlieg/A-Smattering-of-NLP-in-Python/blob/master/A%20Smattering%20of%20NLP%20in%20Python.ipynb) provides a nice overview of NLTK, as does this [notebook from DAT5](https://github.com/justmarkham/DAT5/blob/master/notebooks/14_nlp.ipynb).
-* [spaCy](https://honnibal.github.io/spaCy/) is a newer Python library for text processing that is focused on performance (unlike NLTK).
+* [spaCy](http://spacy.io/) is a newer Python library for text processing that is focused on performance (unlike NLTK).
 * If you want to get serious about NLP, [Stanford CoreNLP](http://nlp.stanford.edu/software/corenlp.shtml) is a suite of tools (written in Java) that is highly regarded.
 * When working with a large text corpus in scikit-learn, [HashingVectorizer](http://scikit-learn.org/stable/modules/feature_extraction.html#vectorizing-a-large-text-corpus-with-the-hashing-trick) is a useful alternative to CountVectorizer.
 * [Automatically Categorizing Yelp Businesses](http://engineeringblog.yelp.com/2015/09/automatically-categorizing-yelp-businesses.html) discusses how Yelp uses NLP and scikit-learn to solve the problem of uncategorized businesses.
@@ -455,7 +456,7 @@ Tuesday | Thursday
 * **Optional:** Keep working on this competition! You can make up to 5 submissions per day, and the competition doesn't close until 6:30pm ET on Tuesday 10/27 (class 21).
 
 **Resources:**
-* For a great overview of the diversity of problems tackled by Kaggle competitions, watch [Kaggle Transforms Data Science Into Competitive Sport](https://www.youtube.com/watch?v=8w4UY66GKcM) (28 minutes) by Jeremy Howard (past president of Kaggle).
+* [Specialist Knowledge Is Useless and Unhelpful](http://www.slate.com/articles/health_and_science/new_scientist/2012/12/kaggle_president_jeremy_howard_amateurs_beat_specialists_in_data_prediction.html) is a brief interview with Jeremy Howard (past president of Kaggle) in which he argues that data science skills are much more important than domain expertise for creating effective predictive models.
 * [Getting in Shape for the Sport of Data Science](https://www.youtube.com/watch?v=kwt6XEh7U3g) (74 minutes), also by Jeremy Howard, contains a lot of tips for competitive machine learning.
 * [Learning from the best](http://blog.kaggle.com/2014/08/01/learning-from-the-best/) is an excellent blog post covering top tips from Kaggle Masters on how to do well on Kaggle.
 * [Feature Engineering Without Domain Expertise](https://www.youtube.com/watch?v=bL4b1sGnILU) (17 minutes), a talk by Kaggle Master Nick Kridler, provides some simple advice about how to iterate quickly and where to spend your time during a Kaggle competition.