Course · Intermediate

Decision Tree and Random Forest Modeling in Python

In this course, you'll learn how to create and implement a Decision Tree, one of the most popular supervised models used in Data Science. You'll also learn to implement the Random Forest algorithm, a powerful prediction technique.

  • Intermediate friendly
  • 6 hrs
  • 4 lessons
  • 1 project
  • Premium

Course overview

Apply decision trees and random forest models to solve classification and regression problems while producing interpretable, high-performing predictions.

What's inside

4 lessons · 1 project

  • 01
    Foundations of Decision Trees

    <p>Learn the universal structure of decision trees and the inner workings of the algorithm used by scikit-learn: CART (Classification and Regression Trees).</p>

    86 min
  • 02
    Building Decision Trees Using Scikit-learn

    Learn to preprocess data to train decision trees, and use scikit-learn to build and visualize both regression and classification trees.

    75 min
  • 03
    Evaluating and Optimizing Decision Trees

    <p>Learn to evaluate the predictive power of both regression and classification trees, along with the different parameters and optimization tools available to improve their efficiency.</p>

    73 min
  • 04
    Cross Validation and Ensemble Techniques for Decision Trees

    <p>Learn advanced approaches for evaluating your decision trees and improve their efficiency, including cross validation, grid search, random forest, and extra trees.</p>

    78 min
  • 05
    Guided Project: Predicting Employee Productivity Using Tree Models Project

    For this project, we'll step into the role of data scientists to determine the best working conditions for maximizing productivity in a garment factory using decision trees and random forests in Python.

    23 min

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