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. The notebook contains everything you see in the video: code, output, images, and comments.

Note: The notebooks in this repository have been updated to use Python 3.6 and scikit-learn 0.19.1. 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.

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

Once you complete this video series, I recommend enrolling in my online course, Machine Learning with Text in Python, to gain a deeper understanding of scikit-learn and Natural Language Processing.

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?
   • 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)

   • 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)

   • 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)

   • 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)

   • 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?

10. Encoding categorical features (video, notebook)

    • Why should you use a Pipeline?
    • How do you encode categorical features with OneHotEncoder?
    • How do you apply OneHotEncoder to selected columns with ColumnTransformer?
    • How do you build and cross-validate a Pipeline?
    • How do you make predictions on new data using a Pipeline?
    • Why should you use scikit-learn (rather than pandas) for preprocessing?

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.