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Machine Learning r Course
Unlock the potential of machine learning with our comprehensive Machine Learning R Course, designed specifically for statistics professionals in Ireland. Delve into data preprocessing, mastering techniques like data splitting, feature scaling, and encoding categorical variables. Hone your skills in data handling, exploratory data analysis, and model fine-tuning. Learn to implement algorithms such as Random Forests and Linear Regression, and evaluate models using cross-validation and metrics like R-squared. Elevate your expertise with practical, top-notch content tailored for real-world applications in the Irish context.
- Master data preprocessing: Split, scale, and encode data efficiently.
- Clean and handle data: Load, inspect, and manage missing values in R.
- Conduct EDA: Visualize distributions and detect outliers effectively.
- Fine-tune models: Optimize algorithms with hyperparameter tuning.
- Evaluate models: Use cross-validation and interpret performance metrics.
from 4 to 360h flexible workload
certificate recognized by the MEC
What will I learn?
Unlock the potential of machine learning with our comprehensive Machine Learning R Course, designed specifically for statistics professionals in Ireland. Delve into data preprocessing, mastering techniques like data splitting, feature scaling, and encoding categorical variables. Hone your skills in data handling, exploratory data analysis, and model fine-tuning. Learn to implement algorithms such as Random Forests and Linear Regression, and evaluate models using cross-validation and metrics like R-squared. Elevate your expertise with practical, top-notch content tailored for real-world applications in the Irish context.
Elevify advantages
Develop skills
- Master data preprocessing: Split, scale, and encode data efficiently.
- Clean and handle data: Load, inspect, and manage missing values in R.
- Conduct EDA: Visualize distributions and detect outliers effectively.
- Fine-tune models: Optimize algorithms with hyperparameter tuning.
- Evaluate models: Use cross-validation and interpret performance metrics.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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