Introduction to machine learning with scikit-learn

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

Entire series

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

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?