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Machine Learning Course
Open di pawa ya masheen learning wit wi kos weh dem design proper for tech pipo dem. Enta deep insai di wok ya kollecting data an to sabi di data using Pandas an NumPy, sabi di plan weh yu fit use for split di data, an check if di model dey wok fine. Learn how to fix di data bifo yu use am, sabi di best model weh yu fit choose, an get sense for regression models like Decision Trees an Random Forests. Make yu hand strong wit wok weh e dey high quality weh e go make sure sey yu ready to face challenge dem for real life. Join now an change yu life!
- Sabi handle data strong strong: Load, look am well well, an choose data wit Pandas an NumPy.
- Do di splitting ya data: Use cross-validation an stratified sampling techniques.
- Check if model dey wok well: Sabi MAE, MSE, an R-squared metrics.
- Fix data well well bifo yu use am: Tek out wetin dey miss an change categorical variables.
- Make model dey wok beta: Tune hyperparameters an use ensemble methods.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open di pawa ya masheen learning wit wi kos weh dem design proper for tech pipo dem. Enta deep insai di wok ya kollecting data an to sabi di data using Pandas an NumPy, sabi di plan weh yu fit use for split di data, an check if di model dey wok fine. Learn how to fix di data bifo yu use am, sabi di best model weh yu fit choose, an get sense for regression models like Decision Trees an Random Forests. Make yu hand strong wit wok weh e dey high quality weh e go make sure sey yu ready to face challenge dem for real life. Join now an change yu life!
Elevify advantages
Develop skills
- Sabi handle data strong strong: Load, look am well well, an choose data wit Pandas an NumPy.
- Do di splitting ya data: Use cross-validation an stratified sampling techniques.
- Check if model dey wok well: Sabi MAE, MSE, an R-squared metrics.
- Fix data well well bifo yu use am: Tek out wetin dey miss an change categorical variables.
- Make model dey wok beta: Tune hyperparameters an use ensemble methods.
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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