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Machine Learning r Course
Unlock the potential of machine learning with our comprehensive Machine Learning R Course, specifically crafted for statistics professionals in India. Delve into data pre-processing, mastering techniques like data splitting, feature scaling, and encoding categorical variables. Sharpen your data handling, exploratory data analysis, and model fine-tuning skills. Learn to implement algorithms such as Random Forests and Linear Regression, and evaluate models using cross-validation and metrics like R-squared. Enhance your expertise with practical, high-quality content tailored for real-world applications relevant to the Indian context.
- Master data pre-processing: 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: Optimise algorithms with hyperparameter tuning.
- Evaluate models: Use cross-validation and interpret performance metrics.
flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Unlock the potential of machine learning with our comprehensive Machine Learning R Course, specifically crafted for statistics professionals in India. Delve into data pre-processing, mastering techniques like data splitting, feature scaling, and encoding categorical variables. Sharpen your data handling, exploratory data analysis, and model fine-tuning skills. Learn to implement algorithms such as Random Forests and Linear Regression, and evaluate models using cross-validation and metrics like R-squared. Enhance your expertise with practical, high-quality content tailored for real-world applications relevant to the Indian context.
Elevify advantages
Develop skills
- Master data pre-processing: 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: Optimise 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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