Skip to main content
HomeMachine LearningMachine Learning with Tree-Based Models in Python

Machine Learning with Tree-Based Models in Python

4.5+
42 reviews
Intermediate

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Start Course for Free
5 Hours15 Videos57 Exercises
84,928 LearnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies


Course Description

Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. By aggregating the predictions of trees that are trained differently, ensemble methods take advantage of the flexibility of trees while reducing their tendency to memorize noise. Ensemble methods are used across a variety of fields and have a proven track record of winning many machine learning competitions. In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.
  1. 1

    Classification and Regression Trees

    Free

    Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

    Play Chapter Now
    Decision tree for classification
    50 xp
    Train your first classification tree
    100 xp
    Evaluate the classification tree
    100 xp
    Logistic regression vs classification tree
    100 xp
    Classification tree Learning
    50 xp
    Growing a classification tree
    50 xp
    Using entropy as a criterion
    100 xp
    Entropy vs Gini index
    100 xp
    Decision tree for regression
    50 xp
    Train your first regression tree
    100 xp
    Evaluate the regression tree
    100 xp
    Linear regression vs regression tree
    100 xp
  2. 2

    The Bias-Variance Tradeoff

    The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. In this chapter, you'll understand how to diagnose the problems of overfitting and underfitting. You'll also be introduced to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

    Play Chapter Now
  3. 3

    Bagging and Random Forests

    Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. In this chapter, you'll understand how bagging can be used to create a tree ensemble. You'll also learn how the random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

    Play Chapter Now
  4. 4

    Boosting

    Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. In this chapter, you'll be introduced to the two boosting methods of AdaBoost and Gradient Boosting.

    Play Chapter Now
  5. 5

    Model Tuning

    The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. In this chapter, you'll learn how to tune the hyperparameters of a tree-based model using grid search cross validation.

    Play Chapter Now

In the following tracks

Associate Data Scientist in PythonMachine Learning Scientist with PythonSupervised Machine Learning in Python

Collaborators

Collaborator's avatar
Kara Woo
Collaborator's avatar
Eunkyung Park
Collaborator's avatar
Sumedh Panchadhar
Elie Kawerk HeadshotElie Kawerk

Senior Data Scientist

Elie is a data scientist with a background in computational quantum physics. His experience encompasses several industries including brick and mortar retail, e-commerce, entertainment, and quick-commerce. He uses a variety of tools and techniques such as machine learning, experimentation, and causal inference to drive business value. His work on a Word2vec-based recommender system has been featured in Amazon Web Service's blog. As a meetup organizer, Elie is passionate about teaching data science and mentoring new-entrants to the field. Elie holds a Phd in physics from Sorbonne University.
See More

Don’t just take our word for it

*4.5
from 42 reviews
71%
12%
14%
2%
0%
Sort by
  • rabbit 1.
    7 months

    Great experience with practically applying concepts learned

  • Vengadesh W.
    9 months

    Very good

  • Joern B.
    10 months

    This course is well organized and keeps the classification as well the regression in the focus. If you download the files you are able to get the same results as doing it online. Never had this before!!! Thanks to Elie Kawerk who set this course up. This course is cristal clear and transparent.

  • Jonathan W.
    11 months

    Great course. I especially like that the instructor used pictures to explain the structure/flow of different kinds of models. It made things a lot clearer. One thing is that I think it would be better if the instructor talks more about in which situations we should use which kinds of models, as such a variety of models in the course may seem confusing without specifying in which scenario should we use them.

  • Kyaw A.
    11 months

    Really, really great course.

"Great experience with practically applying concepts learned"

rabbit 1.

"Very good"

Vengadesh W.

"This course is well organized and keeps the classification as well the regression in the focus. If you download the files you are able to get the same results as doing it online. Never had this before!!! Thanks to Elie Kawerk who set this course up. This course is cristal clear and transparent."

Joern B.

FAQs

Join over 13 million learners and start Machine Learning with Tree-Based Models in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.