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 the data features. DTs are used for both classification and regression and are simple to understand and interpret.
Below, we have listed down the top online courses, YouTube videos and guides for enthusiasts to master decision trees.
Decision trees on CodeAcademy
The course by CodeAcademy focuses on teaching developers how to build and use decision trees and random forests. The course looks at two methods in detail: Gini impurity and Information Gain. Essentially an interactive platform, the course will help developers understand the concepts and the coding components. Topics covered include decision trees, Gini impurity, recursive tree building, information gain and classifying data while testing the concepts on datasets to create various decision trees. The course also offers portfolio projects and quizzes.
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Decision Tree – Theory, Application and Modelling using R on Udemy
The course explains the A-Zs of decision trees and comprises a total of eight hours of lectures from analytics professional Gopal Prasad Malakar. The target audience of the course is industry professionals, and covers topics like decision trees, their applications, advantages, decision tree algorithms, developing the tree in R and interpreting a decision R.
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Decision Trees, Random Forests on Udemy
Start-Tech Academy’s course targets programmers with basic knowledge of languages. The course aims to help individuals understand decision tree algorithms, create a tree in Python, and solve business problems through 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 detailed machine learning playlist are reserved for decision trees and random forest algorithms; lessons 12 and 13, respectively. The lesson includes a half an hour explainer video complemented by texts, diagrams and charts. The decision tree based learnings covered include basic concepts, applications, terminologies, methodologies, algorithm building and how to build DTs in Python.
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Decision tree algorithm lecture by Edureka
The hour-long video by an Edureka professor discusses decision tree algorithms in Python. The video takes developers through the fundamentals of decision tree algorithms and the concepts of classification, classification use-cases, decision tree terminologies, visualising a decision tree and writing it from scratch in Python.
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MathematicalMonk’s Course
Mathematical Monk is a YouTube channel training graduates and upper-level graduates in mathematics. Their ML 2.1, 2.2 and 2.3 explainers as part of the machine learning playlist is 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 headquartered in the US, providing enterprise software to organisations for technical hiring needs. The company also posts machine learning tutorials and practice problems. Their guide on decision trees is a comprehensive textual explanation of the topics and diagrammatic examples and applications. The lessons are taught through real-life problems. It also lays out the coding basics to build a decision tree.
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Decision Trees and Ensemble Methods by Stanford on YouTube
Stanford’s CS229 is a broad introduction to machine learning and statistical pattern recognition. Decision trees and ensemble methods are one of the modules in the course, with a 100-minute video of the lecture. Professor Raphael Townshend, a PhD Candidate and CS229 Head TA, offers lessons on decision trees, general ensembling methods and random forests.
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ISLR Chapter 8: Tree-Based Methods playlist on YouTube
Data School, an online school to learn data science, has put out a playlist comprising five-part videos and two extra lectures discussing decision trees, pruning a decision tree, classification and comparison with linear models, bootstrap aggregation, random forest and booting, and variable importance.
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Machine Learning Lecture 29 at Cornell on YouTube
Kilian Weinberger is an Associate Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in machine earning and authored several papers in the field. The 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 the University of Edinburgh on YouTube
Victor Lavrenko is a lecturer and assistant professor at the University of Edinburgh focused on developing better algorithms for search engines. Victor teaches Introductory Applied Machine Learning (IAML), among other courses at the University of Edinburgh. The module on decision tree has been broken down into short 5-minute explainer videos on YouTube.
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