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Python For Machine Learning Course
Open up the power of Python for machine learning with our full training designed for people who work with technology. You go deep into regression algorithms like Random Forests and Decision Trees, become a master of model testing using things like RMSE and MAE, and learn how to prepare data using methods like feature scaling and encoding. Build up your skills with feature selection ways, writing project reports, and using Python libraries like NumPy and Pandas. Make models work their best with hyperparameter tuning and group methods. Join now to raise your level in machine learning.
- Become a master of regression: Use Random Forests, Decision Trees, and Linear Regression.
- Test models: Use RMSE, MAE, and cross-validation to see how well they perform.
- Prepare data: Scale features, deal with missing data, and encode categorical variables.
- Make models work their best: Use hyperparameter tuning, group methods, and search ways.
- Look at data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand the data.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open up the power of Python for machine learning with our full training designed for people who work with technology. You go deep into regression algorithms like Random Forests and Decision Trees, become a master of model testing using things like RMSE and MAE, and learn how to prepare data using methods like feature scaling and encoding. Build up your skills with feature selection ways, writing project reports, and using Python libraries like NumPy and Pandas. Make models work their best with hyperparameter tuning and group methods. Join now to raise your level in machine learning.
Elevify advantages
Develop skills
- Become a master of regression: Use Random Forests, Decision Trees, and Linear Regression.
- Test models: Use RMSE, MAE, and cross-validation to see how well they perform.
- Prepare data: Scale features, deal with missing data, and encode categorical variables.
- Make models work their best: Use hyperparameter tuning, group methods, and search ways.
- Look at data: Use NumPy, Pandas, Matplotlib, and Seaborn to understand the data.
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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