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Introduction to Machine Learning For Data Science Course
Unlock the power wey data get with our 'Introduction to Machine Learning for Data Science' course, wey we tailor make e fit Business Intelligence professionals. Enter inside important topics like data preprocessing, feature engineering, and how to train model well well. Master better better algorithms like Random Forests and Gradient Boosting make you fit predict sales. Learn how to make your models correct through hyperparameter tuning and how to check am using metrics like MAE and RMSE. Carry your BI skills go up with correct, high-quality insights wey go drive business success.
- Master data cleaning: Make sure say your data dey correct by removing anything wey no follow and errors.
- Develop feature engineering: Create features wey go make sense well well, so your model go perform beta.
- Optimize models: Make am more correct with hyperparameter tuning and compare different algorithms.
- Visualize data insights: Use visualization tools to find insights wey you fit use do something.
- Evaluate models: Measure how e dey go with metrics like MAE and RMSE.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the power wey data get with our 'Introduction to Machine Learning for Data Science' course, wey we tailor make e fit Business Intelligence professionals. Enter inside important topics like data preprocessing, feature engineering, and how to train model well well. Master better better algorithms like Random Forests and Gradient Boosting make you fit predict sales. Learn how to make your models correct through hyperparameter tuning and how to check am using metrics like MAE and RMSE. Carry your BI skills go up with correct, high-quality insights wey go drive business success.
Elevify advantages
Develop skills
- Master data cleaning: Make sure say your data dey correct by removing anything wey no follow and errors.
- Develop feature engineering: Create features wey go make sense well well, so your model go perform beta.
- Optimize models: Make am more correct with hyperparameter tuning and compare different algorithms.
- Visualize data insights: Use visualization tools to find insights wey you fit use do something.
- Evaluate models: Measure how e dey go with metrics like MAE and RMSE.
Suggested summary
Before starting, you can change the chapters and 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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