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Data Science Machine Learning Course
Open eye to the power wey data get with our Data Science and Machine Learning Training, wey dem tailor make e fit Business Intelligence professionals. Enter inside feature engineering, and sabi time-based and domain-specific strategies well well. Boost your skills with data preprocessing, handle missing values like oga, and encode categorical variables like correct guy. Explore machine learning algorithms like Decision Trees and Gradient Boosting. Learn how to train model, evaluate am, and deploy am, so you fit join am with business processes without stress. Carry your BI expertise to another level with correct, high-quality insights.
- Master feature engineering: Create features wey dey ginger and wey make sense for your domain.
- Deploy models seamlessly: Join am with business processes sharp sharp.
- Evaluate models precisely: Use RMSE, MAE, and cross-validation techniques to check am well.
- Preprocess data effectively: Clean am, encode am, and handle missing values like a boss.
- Optimize algorithms: Tune hyperparameters and compare how the model dey perform.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open eye to the power wey data get with our Data Science and Machine Learning Training, wey dem tailor make e fit Business Intelligence professionals. Enter inside feature engineering, and sabi time-based and domain-specific strategies well well. Boost your skills with data preprocessing, handle missing values like oga, and encode categorical variables like correct guy. Explore machine learning algorithms like Decision Trees and Gradient Boosting. Learn how to train model, evaluate am, and deploy am, so you fit join am with business processes without stress. Carry your BI expertise to another level with correct, high-quality insights.
Elevify advantages
Develop skills
- Master feature engineering: Create features wey dey ginger and wey make sense for your domain.
- Deploy models seamlessly: Join am with business processes sharp sharp.
- Evaluate models precisely: Use RMSE, MAE, and cross-validation techniques to check am well.
- Preprocess data effectively: Clean am, encode am, and handle missing values like a boss.
- Optimize algorithms: Tune hyperparameters and compare how the model dey perform.
Suggested summary
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