🎓 Advanced Machine Learning with R

Welcome to the Advanced Machine Learning with R course! This program is designed for data scientists and analysts who want to deepen their skills in applying machine learning techniques using the powerful R programming language. 🚀

📚 What You'll Learn

• Master advanced algorithms: Random Forests, Gradient Boosting, and Neural Networks
• Implement feature engineering and model optimization techniques
• Analyze real-world datasets with R's ML libraries (e.g., caret, randomForest)
• Build predictive models and interpret results using visualization tools

🧠 Course Outline

1. Introduction to R for ML 📈

   • R environment setup and packages
   • Data preprocessing techniques
   

2. Supervised Learning Deep Dive 🧪

   • Regression and classification advanced methods
   • Cross-validation and hyperparameter tuning
   

3. Unsupervised Learning & Dimensionality Reduction 🌀

   • Clustering algorithms (K-means, DBSCAN)
   • Principal Component Analysis (PCA) implementation
   

4. Model Evaluation & Deployment 🧾

   • Metrics: Accuracy, Precision, Recall, F1 Score
   • Deploying models with caret and shiny
   

📚 Recommended Resources

• R Programming Foundations - Build your R basics first!
• "An Introduction to Statistical Learning" by Gareth James (opens new window)
• Kaggle Machine Learning Tutorials

🌐 Extend Your Knowledge

Explore related topics like Data Visualization with R or Big Data Analytics to strengthen your skillset.

Happy coding! 🧠💻