This repo contains IPython notebooks from my scikit-learn video series, as seen on Kaggle's blog.
- Watch the entire series (YouTube playlist)
- View the IPython Notebooks (nbviewer)
- Read the blog posts (Kaggle's blog)
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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"?
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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?
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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?
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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?
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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?
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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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Cross-validation for parameter tuning, model selection, and feature selection (video, notebook, blog post)
- 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?
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Efficiently searching for optimal tuning parameters (video, notebook, blog post)
- 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?