Top Resources for Learning Decision Trees in 2022

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Decision trees are a supervised learning method used to build a model that predicts the value of a target variable by learning simple decision rules from data characteristics. DTs are used for both classification and regression and are simple to understand and interpret.

Below, we’ve listed the best online courses, YouTube videos, and guides for enthusiasts to master decision trees.

Decision trees on CodeAcademy

The CodeAcademy course focuses on teaching developers how to create and use decision trees and random forests. The course examines two methods in detail: Gini impurity and information gain. Essentially an interactive platform, the course will help developers understand coding concepts and components. Topics covered include decision trees, Gini impurity, building recursive trees, gaining information, and classifying data while testing concepts on datasets to build various decision trees. The course also offers portfolio projects and quizzes.

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Decision Tree – Theory, Application, and Modeling Using R on Udemy

The course explains the A to Z of decision trees and includes a total of eight hours of lectures from analysis professional Gopal Prasad Malakar. The target audience for the course is industry professionals and covers topics such as decision trees, their applications, benefits, decision tree algorithms, tree development in R, and interpretation of a decision R.

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Decision Trees, Random Forests on Udemy

The Start-Tech Academy course is aimed at programmers with basic knowledge of languages. The course aims to help individuals understand decision tree algorithms, build a tree in Python, and solve business problems using decision trees. It covers the basics of ML and Python before moving on to decision trees.

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The Best Guide on How to Implement Decision Tree in Python on Simplilearn

A few modules in Simplilearn’s in-depth machine learning playlist are reserved for Decision Trees and Random Forest Algorithms; lessons 12 and 13, respectively. The lesson includes a half-hour explanatory video supplemented with texts, diagrams and graphics. Decision tree-based learnings covered include basic concepts, applications, terminologies, methodologies, building algorithms, and how to build DTs in Python.

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Lecture on Decision Tree Algorithm by Edureka

An hour-long video from an Edureka professor discusses decision tree algorithms in Python. The video introduces developers to the fundamentals of decision tree algorithms and classification concepts, classification use cases, decision tree terminologies, visualizing a decision tree and writing it from from zero in Python.

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MathematicalMonk course

Mathematical Monk is a YouTube channel training graduates and graduates in mathematics. Their ML Explainers 2.1, 2.2, and 2.3 as part of the Machine Learning Playlist provide a basic introduction to decision trees for regression using the CART approach. The concept is explained through diagrams and explanations.

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Decision tree on HackerEarth

HackerEarth is an Indian software company based in the United States, providing enterprise software to organizations for technical recruitment needs. The company also publishes machine learning tutorials and practice problems. Their guide to decision trees is a full textual explanation of the topics and schematic examples and applications. Lessons are taught through real problems. It also presents the basics of coding to build a decision tree.

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Decision Trees and Ensemble Methods by Stanford on YouTube

Stanford’s CS229 is a general introduction to machine learning and statistical pattern recognition. Decision Trees and Ensemble Methods is one of the course modules, along with a 100-minute video of the lecture. Professor Raphael Townshend, PhD Candidate and Chief TA of CS229, offers courses on Decision Trees, General Assembly Methods and Random Forests.

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ISLR Chapter 8: YouTube Tree-Based Methods Playlist

Data School, an online school for learning data science, has released a playlist including five-part videos and two additional lectures on decision trees, pruning a decision tree, classification, and comparison with linear models, bootstrap aggregation, random forest and booting, and variable significance.

Find the playlist here.

Machine Learning Lecture 29 at Cornell on YouTube

Kilian Weinberger is an Associate Professor in the Department of Computer Science at Cornell University. He got his doctorate. from the University of Pennsylvania in automatic earnings and author of several articles in the field. Youtube Tutorial is his recorded lectures on Decision Trees, Impurity Functions, ID3 Algorithm and Parametric Algorithms.

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Decision Tree Learning Playlist by University of Edinburgh on YouTube

Victor Lavrenko is a senior lecturer and adjunct professor at the University of Edinburgh, specializing in developing better algorithms for search engines. Victor teaches Introduction to Applied Machine Learning (IAML), among other courses at the University of Edinburgh. The decision tree module has been broken down into short 5-minute explainer videos on YouTube.

Find the playlist here

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