Udemy MOOC / Non-credit 0

R tidymodels part 2: Beyond linear regression

Name: R tidymodels part 2: Beyond linear regression
Price: 119.99 USD
Availability: InStock

About this course

You've built your first predictive models. You understand linear regression and regularization. Now it’s time to level up.This course is designed for learners who want to go beyond simple models and tackle non-linear relationships, ensemble algorithms, and real-world modeling challenges with confidence.What You'll Learn?In this course, we remain in the regression domain but expand your modeling toolbox with powerful new algorithms and modeling strategies:Use k-nearest neighbors (KNN) for flexible, non-parametric regressionBuild decision trees for interpretable, rule-based modelsApply random forests for robust ensemble modelingHarness the power of XGBoost and LightGBM, two of the fastest and most powerful tree-based learnersUnderstand the principles behind bagging and boostingLearn how parallel processing speeds up model tuning and resamplingTune hyperparameters efficiently with grids and a Bayesian iterative search approachCompare models using consistent metrics across algorithmsStructure your modeling workflow for scalability, readability, and reproducibilityAnd to wrap it all up, you’ll complete a final modeling project, where you build a predictive model on new data, applying everything you’ve learned.Why Take This Course?Modern dat

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What you'll learn

use k-nearest neighbors for flexible regression
build decision trees for rule-based models
apply random forests for ensemble modeling
harness XGBoost and LightGBM for tree-based learning
understand bagging and boosting
implement parallel processing for model tuning
efficiently tune hyperparameters with grids and Bayesian search
compare models using consistent metrics

Machine Learning #predictive modeling #ensemble learning #hyperparameter tuning #decision trees #xgboost #lightgbm #random forests #parallel processing #k nearest neighbors #boosting #bagging

$119.99

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