add video 9 and change notebook links

README.md

@@ -10,35 +10,35 @@ This repo contains IPython notebooks from my scikit-learn video series, as seen
 
 ## Individual videos
 
-1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/01_machine_learning_intro.ipynb), [blog post](http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/))
+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?
     - What are the two main categories of machine learning?
     - What are some examples of machine learning?
     - How does machine learning "work"?
 
-2. Setting up Python for machine learning: scikit-learn and IPython Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/02_machine_learning_setup.ipynb), [blog post](http://blog.kaggle.com/2015/04/15/scikit-learn-video-2-setting-up-python-for-machine-learning/))
+2. Setting up Python for machine learning: scikit-learn and IPython Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb), [blog post](http://blog.kaggle.com/2015/04/15/scikit-learn-video-2-setting-up-python-for-machine-learning/))
     - What are the benefits and drawbacks of scikit-learn?
     - How do I install scikit-learn?
     - How do I use the IPython Notebook?
     - What are some good resources for learning Python?
 
-3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/03_getting_started_with_iris.ipynb), [blog post](http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/))
+3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](03_getting_started_with_iris.ipynb), [blog post](http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/))
     - What is the famous iris dataset, and how does it relate to machine learning?
     - How do we load the iris dataset into scikit-learn?
     - How do we describe a dataset using machine learning terminology?
     - What are scikit-learn's four key requirements for working with data?
 
-4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/04_model_training.ipynb), [blog post](http://blog.kaggle.com/2015/04/30/scikit-learn-video-4-model-training-and-prediction-with-k-nearest-neighbors/))
+4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb), [blog post](http://blog.kaggle.com/2015/04/30/scikit-learn-video-4-model-training-and-prediction-with-k-nearest-neighbors/))
     - What is the K-nearest neighbors classification model?
     - What are the four steps for model training and prediction in scikit-learn?
     - How can I apply this pattern to other machine learning models?
 
-5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/05_model_evaluation.ipynb), [blog post](http://blog.kaggle.com/2015/05/14/scikit-learn-video-5-choosing-a-machine-learning-model/))
+5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb), [blog post](http://blog.kaggle.com/2015/05/14/scikit-learn-video-5-choosing-a-machine-learning-model/))
     - How do I choose which model to use for my supervised learning task?
     - How do I choose the best tuning parameters for that model?
     - How do I estimate the likely performance of my model on out-of-sample data?
 
-6. Data science pipeline: pandas, seaborn, scikit-learn ([video](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/06_linear_regression.ipynb), [blog post](http://blog.kaggle.com/2015/05/28/scikit-learn-video-6-linear-regression-plus-pandas-seaborn/))
+6. Data science pipeline: pandas, seaborn, scikit-learn ([video](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6), [notebook](06_linear_regression.ipynb), [blog post](http://blog.kaggle.com/2015/05/28/scikit-learn-video-6-linear-regression-plus-pandas-seaborn/))
     - How do I use the pandas library to read data into Python?
     - How do I use the seaborn library to visualize data?
     - What is linear regression, and how does it work?
@@ -46,15 +46,24 @@ This repo contains IPython notebooks from my scikit-learn video series, as seen
     - What are some evaluation metrics for regression problems?
     - How do I choose which features to include in my model?
 
-7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/07_cross_validation.ipynb), [blog post](http://blog.kaggle.com/2015/06/29/scikit-learn-video-7-optimizing-your-model-with-cross-validation/))
+7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](07_cross_validation.ipynb), [blog post](http://blog.kaggle.com/2015/06/29/scikit-learn-video-7-optimizing-your-model-with-cross-validation/))
     - What is the drawback of using the train/test split procedure for model evaluation?
     - How does K-fold cross-validation overcome this limitation?
     - How can cross-validation be used for selecting tuning parameters, choosing between models, and selecting features?
     - What are some possible improvements to cross-validation?
 
-8. Efficiently searching for optimal tuning parameters ([video](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8), [notebook](http://nbviewer.ipython.org/github/justmarkham/scikit-learn-videos/blob/master/08_grid_search.ipynb), [blog post](http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/))
+8. Efficiently searching for optimal tuning parameters ([video](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8), [notebook](08_grid_search.ipynb), [blog post](http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/))
     - How can K-fold cross-validation be used to search for an optimal tuning parameter?
     - How can this process be made more efficient?
     - How do you search for multiple tuning parameters at once?
     - What do you do with those tuning parameters before making real predictions?
     - How can the computational expense of this process be reduced?
+
+9. Evaluating a classification model ([video](https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9), [notebook](09_classification_metrics.ipynb), blog post)
+    - What is the purpose of model evaluation, and what are some common evaluation procedures?
+    - What is the usage of classification accuracy, and what are its limitations?
+    - How does a confusion matrix describe the performance of a classifier?
+    - What metrics can be computed from a confusion matrix?
+    - 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?