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Machine Learning r Course
Unlock the power of Machine Learning with our Machine Learning R Course, we don adapt am small for statistics people like you. Learn how to prepare your data well well, sabi techniques like how to split data, scale features, and change categorical variables to numbers wey computer fit understand. Get better for handling data, doing Exploratory Data Analysis (EDA), and fine-tuning your models. Learn how to use algorithms like Random Forests and Linear Regression, and check how good your models dey using cross-validation and things like R-squared. Sharpen your skills with practical, correct content wey go help you for real work.
- Master data preprocessing: Split, scale, and encode data quick quick.
- Clean and handle data: Load data, check am well, and manage any empty spaces inside the data for R.
- Conduct EDA: See how the data dey spread out with graphs and find any strange things inside.
- Fine-tune models: Make your algorithms work better by adjusting the settings.
- Evaluate models: Use cross-validation to check how the model dey perform and understand the results.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the power of Machine Learning with our Machine Learning R Course, we don adapt am small for statistics people like you. Learn how to prepare your data well well, sabi techniques like how to split data, scale features, and change categorical variables to numbers wey computer fit understand. Get better for handling data, doing Exploratory Data Analysis (EDA), and fine-tuning your models. Learn how to use algorithms like Random Forests and Linear Regression, and check how good your models dey using cross-validation and things like R-squared. Sharpen your skills with practical, correct content wey go help you for real work.
Elevify advantages
Develop skills
- Master data preprocessing: Split, scale, and encode data quick quick.
- Clean and handle data: Load data, check am well, and manage any empty spaces inside the data for R.
- Conduct EDA: See how the data dey spread out with graphs and find any strange things inside.
- Fine-tune models: Make your algorithms work better by adjusting the settings.
- Evaluate models: Use cross-validation to check how the model dey perform and understand the results.
Suggested summary
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