Skip to content

cafew/scikit-learn-videos

Folders and files

NameName
Last commit message
Last commit date

Latest commit

6443961 · May 13, 2015

History

14 Commits
May 13, 2015
Apr 8, 2015
Apr 8, 2015
Apr 8, 2015
Apr 15, 2015
Apr 21, 2015
Apr 29, 2015
May 13, 2015
May 1, 2015

Repository files navigation

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?

About

Jupyter notebooks from the scikit-learn video series

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.5%
  • CSS 0.5%