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Python For Machine Learning Course
Open di power of Python for machine learning wit dis full training we design for people we sabi technology well-well. We go dig inside regression like Random Forests and Decision Trees, we go learn how to check how good di model dey wit things like RMSE and MAE, and we go see how to clean di data before we use am, like how to scale di features and encode dem. We go make we sabi di way to pick di correct features, how to write report on di project, and how to use Python library like NumPy and Pandas. We go fine-tune di models to make dem work betta wit hyperparameter tuning and ensemble methods. Join now so dat you go sabi machine learning pass as you sabi now.
- Sabi Regression well-well: Use Random Forests, Decision Trees, and Linear Regression.
- Check How Good di Model Be: Use RMSE, MAE, and cross-validation to know how di model dey perform.
- Clean Data Well-Well: Scale features, manage data wey miss, and encode categorical variables.
- Make Di Model Work Fine: Do hyperparameter tuning, ensemble methods, and find betta ways.
- Understand Di Data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand wetin di data dey talk.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open di power of Python for machine learning wit dis full training we design for people we sabi technology well-well. We go dig inside regression like Random Forests and Decision Trees, we go learn how to check how good di model dey wit things like RMSE and MAE, and we go see how to clean di data before we use am, like how to scale di features and encode dem. We go make we sabi di way to pick di correct features, how to write report on di project, and how to use Python library like NumPy and Pandas. We go fine-tune di models to make dem work betta wit hyperparameter tuning and ensemble methods. Join now so dat you go sabi machine learning pass as you sabi now.
Elevify advantages
Develop skills
- Sabi Regression well-well: Use Random Forests, Decision Trees, and Linear Regression.
- Check How Good di Model Be: Use RMSE, MAE, and cross-validation to know how di model dey perform.
- Clean Data Well-Well: Scale features, manage data wey miss, and encode categorical variables.
- Make Di Model Work Fine: Do hyperparameter tuning, ensemble methods, and find betta ways.
- Understand Di Data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand wetin di data dey talk.
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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