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Ensemble Methods in Python

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

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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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In the following Tracks

Supervised Machine Learning in Python

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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 in Python

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datasets

App ratingsApp reviewsGame of ThronesPokémonSECOM (Semiconductor Manufacturing)TMDb (The Movie Database)

collaborators

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

Data Scientist at Appodeal

Román de las Heras is a seasoned Data Scientist, currently working at Appodeal. He studied Systems Engineering and took a simultaneous Math degree with Computer Science at the National Autonomous University of Honduras (UNAH). The ensemble of these two careers drove him into data science. His daily work includes developing ML models, building recommendation engines and ranking systems, 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 "learning by doing".
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