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Machine Algorithms in R

Track :

Programming

Lessons no : 31

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What will you learn in this course?
  • Master machine learning algorithms implementation in R for data analysis and predictive modeling
  • Apply R programming skills to develop and optimize machine learning models for real-world datasets
  • Utilize R libraries and packages for machine learning, including caret, randomForest, and xgboost
  • Analyze and visualize data effectively using R to enhance machine learning insights and decision-making
  • Implement supervised and unsupervised learning algorithms in R for classification, regression, and clustering tasks
  • Evaluate model performance in R using metrics like accuracy, precision, recall, and ROC-AUC
  • Preprocess and clean data in R to improve machine learning model accuracy and robustness
  • Automate machine learning workflows in R for scalable and efficient data analysis projects
  • Troubleshoot common issues in R machine learning models to ensure reliable and valid results
  • Integrate R machine learning models into larger data analysis pipelines for comprehensive insights

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Lessons | 31


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R is an an open source environment for statistical programming and visualization. R is a number of things, which might be confusing at first. R is a computer language. It is a variant of Lisp and you can write programs in it .