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Ensemble Methods in Python

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4 hours
4,050 XP
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Course Description

Continue your machine learning journey by diving into the wonderful world of ensemble learning methods! These are an exciting class of machine learning techniques that combine multiple individual algorithms to boost performance and solve complex problems at scale across different industries. Ensemble techniques regularly win online machine learning competitions as well! In this course, you’ll learn all about these advanced ensemble techniques, such as bagging, boosting, and stacking. You’ll apply them to real-world datasets using cutting edge Python machine learning libraries such as scikit-learn, XGBoost, CatBoost, and mlxtend.
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  1. 1

    Combining Multiple Models

    Free

    Do you struggle to determine which of the models you built is the best for your problem? You should give up on that, and use them all instead! In this chapter, you'll learn how to combine multiple models into one using "Voting" and "Averaging". You'll use these to predict the ratings of apps on the Google Play Store, whether or not a Pokémon is legendary, and which characters are going to die in Game of Thrones!

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    Introduction to ensemble methods
    50 xp
    Exploring Google apps data
    50 xp
    Predicting the rating of an app
    100 xp
    Voting
    50 xp
    Choosing the best model
    100 xp
    Assembling your first ensemble
    100 xp
    Evaluating your ensemble
    100 xp
    Averaging
    50 xp
    Journey to Westeros
    50 xp
    Predicting GoT deaths
    100 xp
    Soft vs. hard voting
    100 xp
  2. 4

    Stacking

    Get ready to see how things stack up! In this final chapter you'll learn about the stacking ensemble method. You'll learn how to implement it using scikit-learn as well as with the mlxtend library! You'll apply stacking to predict the edibility of North American mushrooms, and revisit the ratings of Google apps with this more advanced approach.

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In the following tracks

Supervised Machine Learning

Collaborators

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Hillary Green-Lerman
Collaborator's avatar
Yashas Roy
Román de las Heras HeadshotRomán de las Heras

Senior Data Scientist at Chartboost

Román de las Heras is a Senior Data Scientist at Chartboost. He studied Systems Engineering and took a simultaneous degree in Mathematics with Computer Science Orientation at the National Autonomous University of Honduras (UNAH). The ensemble of these two careers is what drove him into the world of data science. His daily work includes developing machine learning models, applying time series techniques to financial forecasting, training junior team members on the field, and doing ad-hoc data analyses to present results and insights. He is also a passionate and experienced educator, as well as a strong believer in "learn by doing".
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