Interactive Course

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.

  • 4 hours
  • 15 Videos
  • 52 Exercises
  • 630 Participants
  • 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.

  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!

  2. Boosting

    Boosting is class of ensemble learning algorithms that includes award-winning models such as AdaBoost. In this chapter, you'll learn about this award-winning model, and use it to predict the revenue of award-winning movies! You'll also learn about gradient boosting algorithms such as CatBoost and XGBoost.

  3. Bagging

    Bagging is the ensemble method behind powerful machine learning algorithms such as random forests. In this chapter you'll learn the theory behind this technique and build your own bagging models using scikit-learn.

  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 from scratch as well as using 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.

  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!

  2. Bagging

    Bagging is the ensemble method behind powerful machine learning algorithms such as random forests. In this chapter you'll learn the theory behind this technique and build your own bagging models using scikit-learn.

  3. Boosting

    Boosting is class of ensemble learning algorithms that includes award-winning models such as AdaBoost. In this chapter, you'll learn about this award-winning model, and use it to predict the revenue of award-winning movies! You'll also learn about gradient boosting algorithms such as CatBoost and XGBoost.

  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 from scratch as well as using 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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Román de las Heras
Román de las Heras

Data Scientist at SAP and Data Science Team Lead at Agile Solutions

Román de las Heras is a Data Scientist at SAP Digital Interconnect and a Data Science Team Lead at Agile Solutions. 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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