Decision Tree Learning

Decision Tree Learning is a method of approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Coursera's Decision Tree Learning catalogue will guide you in understanding this supervised learning method extensively used in machine learning and data mining. You'll learn how to build, visualize, and optimally prune decision trees for prediction and classification. This catalogue will also teach you about attribute selection measures, overfitting, randomness, and ensemble methods within decision tree learning. In mastering this skill, you'll be equipped to solve complex problems in areas such as finance, healthcare, and natural language processing using decision tree learning algorithms.
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Results for "decision tree learning"

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    DeepLearning.AI

    Skills you'll gain: Deep Learning, Statistical Analysis, Clinical Trials, Risk Modeling, Data Analysis, Precision Medicine, Decision Tree Learning, Predictive Modeling, Applied Machine Learning, Feature Engineering, Patient Treatment, Image Analysis, AI Personalization, Diagnostic Radiology, Machine Learning, Random Forest Algorithm, Forecasting, Data Processing, Artificial Intelligence, Tensorflow

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    University of California San Diego

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    Duke University

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  • Status: Preview

    Sungkyunkwan University

    Skills you'll gain: Data Processing, Portfolio Management, Investment Management, Classification And Regression Tree (CART), Statistical Machine Learning, Investments, Machine Learning Algorithms, Applied Machine Learning, R Programming, Feature Engineering, Machine Learning, Financial Modeling, Predictive Modeling, Decision Tree Learning, Random Forest Algorithm, Asset Management

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