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Machine Learning Course
Unlock the power of machine learning with our in-depth course designed for technology professionals here in Namibia. Get stuck in to data collection and exploration using Pandas and NumPy, become a pro at dataset splitting strategies, and properly evaluate models. Learn the essential data cleaning techniques, explore more advanced model selection, and get proper insights into regression models like Decision Trees and Random Forests. Sharpen your skills with practical, top-notch content that makes sure you're ready to handle real-world problems. Join now and boost your career!
- Master data handling: Load, check out, and select datasets using Pandas and NumPy.
- Implement dataset splitting: Use cross-validation and stratified sampling.
- Evaluate how well models perform: Understand MAE, MSE, and R-squared.
- Clean your data properly: Deal with missing values and encode categorical variables.
- Fine-tune models: Adjust hyperparameters and make use of ensemble methods.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the power of machine learning with our in-depth course designed for technology professionals here in Namibia. Get stuck in to data collection and exploration using Pandas and NumPy, become a pro at dataset splitting strategies, and properly evaluate models. Learn the essential data cleaning techniques, explore more advanced model selection, and get proper insights into regression models like Decision Trees and Random Forests. Sharpen your skills with practical, top-notch content that makes sure you're ready to handle real-world problems. Join now and boost your career!
Elevify advantages
Develop skills
- Master data handling: Load, check out, and select datasets using Pandas and NumPy.
- Implement dataset splitting: Use cross-validation and stratified sampling.
- Evaluate how well models perform: Understand MAE, MSE, and R-squared.
- Clean your data properly: Deal with missing values and encode categorical variables.
- Fine-tune models: Adjust hyperparameters and make use of ensemble methods.
Suggested summary
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