hijaek/scikit-learn-videos

IPython notebooks from the scikit-learn video series

Introduction to machine learning with scikit-learn

This repo contains IPython notebooks from my scikit-learn video series, as seen on Kaggle's blog.

Want to master scikit-learn? I teach an online course, Machine Learning with Text in Python. Starts April 9!

Entire series

• Read the blog posts (Kaggle's blog)
• Watch the entire series (YouTube playlist)
• View the IPython Notebooks (nbviewer)
• Run the IPython Notebooks online (binder)

Individual videos

1. What is machine learning, and how does it work? (video, notebook, blog post)

   • 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, notebook, blog post)

   • 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, notebook, blog post)

   • 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, notebook, blog post)

   • 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, notebook, blog post)

   • 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, notebook, blog post)

   • 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?
   • How do I train and interpret a linear regression model in scikit-learn?
   • 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, notebook, blog post)

   • 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, notebook, blog post)

   • 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, notebook, 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?