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Python For Machine Learning Course
Unlock di power wey Python get for machine learning wit wi proper course wey dem design for technology people dem. Enter inside regression algorithms like Random Forests and Decision Trees, sabi model evaluation metrics like RMSE and MAE well-well, and explore data preprocessing techniques wey include feature scaling and encoding. Mek yu skills strong wit feature selection methods, project documentation, and Python libraries like NumPy and Pandas. Mek models dem work beta wit hyperparameter tuning and ensemble methods. Join now for mek yu sabi pass for machine learning.
- Master regression: Implement Random Forests, Decision Trees, and Linear Regression (Sabi regression well-well: Implement Random Forests, Decision Trees, and Linear Regression).
- Evaluate models: Use RMSE, MAE, and cross-validation for performance metrics (Check model dem well: Use RMSE, MAE, and cross-validation for performance metrics).
- Preprocess data: Scale features, handle missing data, and encode categorical variables (Prepare data well-well: Scale features, handle missing data, and encode categorical variables).
- Optimize models: Apply hyperparameter tuning, ensemble methods, and search strategies (Mek model dem work fine: Apply hyperparameter tuning, ensemble methods, and search strategies).
- Analyze data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights (Look data inside-out: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights).
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock di power wey Python get for machine learning wit wi proper course wey dem design for technology people dem. Enter inside regression algorithms like Random Forests and Decision Trees, sabi model evaluation metrics like RMSE and MAE well-well, and explore data preprocessing techniques wey include feature scaling and encoding. Mek yu skills strong wit feature selection methods, project documentation, and Python libraries like NumPy and Pandas. Mek models dem work beta wit hyperparameter tuning and ensemble methods. Join now for mek yu sabi pass for machine learning.
Elevify advantages
Develop skills
- Master regression: Implement Random Forests, Decision Trees, and Linear Regression (Sabi regression well-well: Implement Random Forests, Decision Trees, and Linear Regression).
- Evaluate models: Use RMSE, MAE, and cross-validation for performance metrics (Check model dem well: Use RMSE, MAE, and cross-validation for performance metrics).
- Preprocess data: Scale features, handle missing data, and encode categorical variables (Prepare data well-well: Scale features, handle missing data, and encode categorical variables).
- Optimize models: Apply hyperparameter tuning, ensemble methods, and search strategies (Mek model dem work fine: Apply hyperparameter tuning, ensemble methods, and search strategies).
- Analyze data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights (Look data inside-out: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights).
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