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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

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?

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IPython notebooks from the scikit-learn video series

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