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Machine Learning r Course
Unlock the power of machine learning with our proper Machine Learning R Course, wey dem design am special for statistics people like you. Enter inside data preprocessing, and become master for techniques like data splitting, feature scaling, and encoding categorical variables. Improve your skills for how to handle data, exploratory data analysis, and model fine-tuning. Learn how to do algorithms like Random Forests and Linear Regression, and check whether your models good using cross-validation and metrics like R-squared. Make your expertise top-notch with correct, high-quality things wey dey inside, all tailor-made for work wey dey be real for town.
- Master data preprocessing: Split, scale, and encode data sharp sharp.
- Clean and handle data: Load data, check am well, and solve any missing value problems for inside R.
- Conduct EDA: Show distributions and catch outliers quick quick.
- Fine-tune models: Make algorithms perform beta with hyperparameter tuning.
- Evaluate models: Use cross-validation and understand the performance metrics.
flexible workload of 4 to 360h
certificate recognized by the MEC
What will I learn?
Unlock the power of machine learning with our proper Machine Learning R Course, wey dem design am special for statistics people like you. Enter inside data preprocessing, and become master for techniques like data splitting, feature scaling, and encoding categorical variables. Improve your skills for how to handle data, exploratory data analysis, and model fine-tuning. Learn how to do algorithms like Random Forests and Linear Regression, and check whether your models good using cross-validation and metrics like R-squared. Make your expertise top-notch with correct, high-quality things wey dey inside, all tailor-made for work wey dey be real for town.
Elevify advantages
Develop skills
- Master data preprocessing: Split, scale, and encode data sharp sharp.
- Clean and handle data: Load data, check am well, and solve any missing value problems for inside R.
- Conduct EDA: Show distributions and catch outliers quick quick.
- Fine-tune models: Make algorithms perform beta with hyperparameter tuning.
- Evaluate models: Use cross-validation and understand the 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 workloadWhat our students say
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