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Data Science Machine Learning Course
Open up the power weh data get with wi Data Science Machine Learning Training, weh dem tailor fit Business Intelligence people. Tek a deep dive inside feature engineering, and master how time dey waka plus strategies weh dem specific to di area weh you dey work. Make una sharpen una skills with data preprocessing, handle missing values well, and encode categorical variables. Learn about machine learning algorithms like Decision Trees and Gradient Boosting. Learn how to train model, evaluate dem, and put dem to work, so dem fit join business processes easy easy. Make una BI skills go up with practical, high-quality knowledge.
- Master feature engineering: Create features weh dem strong and specific to di area weh you dey work.
- Deploy models seamlessly: Make dem join business processes quick quick.
- Evaluate models precisely: Use RMSE, MAE, and cross-validation ways.
- Preprocess data effectively: Clean am, encode am, and handle any values weh dem lost.
- Optimize algorithms: Tune hyperparameters and compare how di models dey do work.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open up the power weh data get with wi Data Science Machine Learning Training, weh dem tailor fit Business Intelligence people. Tek a deep dive inside feature engineering, and master how time dey waka plus strategies weh dem specific to di area weh you dey work. Make una sharpen una skills with data preprocessing, handle missing values well, and encode categorical variables. Learn about machine learning algorithms like Decision Trees and Gradient Boosting. Learn how to train model, evaluate dem, and put dem to work, so dem fit join business processes easy easy. Make una BI skills go up with practical, high-quality knowledge.
Elevify advantages
Develop skills
- Master feature engineering: Create features weh dem strong and specific to di area weh you dey work.
- Deploy models seamlessly: Make dem join business processes quick quick.
- Evaluate models precisely: Use RMSE, MAE, and cross-validation ways.
- Preprocess data effectively: Clean am, encode am, and handle any values weh dem lost.
- Optimize algorithms: Tune hyperparameters and compare how di models dey do work.
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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