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Kevin Markham 2019-11-12 10:37:40 -05:00
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# 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 **9 video tutorials** totaling 4 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments.
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](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/).
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- What is the purpose of an ROC curve?
- How does Area Under the Curve (AUC) differ from classification accuracy?
10. Encoding categorical features ([video](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10), [notebook](10_categorical_features.ipynb))
- 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](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10) on YouTube.