Introduction to Machine Learning with scikit-learn

This video series will teach you how to solve Machine Learning problems using Python's popular scikit-learn library. There are 10 video tutorials totaling 4.5 hours, each with a corresponding Jupyter notebook.

You can watch the entire series on YouTube and view all of the notebooks using nbviewer.

The series is also available as a free online course that includes updated content, quizzes, and a certificate of completion.

Note: The notebooks in this repository have been updated to use Python 3.9.1 and scikit-learn 0.23.2. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the archive branch. You can read about how I updated the code in this blog post.

Table of Contents

1. What is Machine Learning, and how does it work? (video, notebook)

   • 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 Jupyter Notebook (video, notebook)

   • What are the benefits and drawbacks of scikit-learn?
   • How do I install scikit-learn?
   • How do I use the Jupyter Notebook?
   • What are some good resources for learning Python?

3. Getting started in scikit-learn with the famous iris dataset (video, notebook)

   • 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)

   • 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)

   • How do I choose which model to use for my supervised learning task?
   • How do I choose the best tuning parameters for that model?

Bonus Video

At the PyCon 2016 conference, I taught a 3-hour tutorial that builds upon this video series and focuses on text-based data. You can watch the tutorial video on YouTube.

Here are the topics I covered:

1. Model building in scikit-learn (refresher)
2. Representing text as numerical data
3. Reading a text-based dataset into pandas
4. Vectorizing our dataset
5. Building and evaluating a model
6. Comparing models
7. Examining a model for further insight
8. Practicing this workflow on another dataset
9. Tuning the vectorizer (discussion)

Visit this GitHub repository to access the tutorial notebooks and many other recommended resources.