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Python For Machine Learning Course
Unlock the potential of Python for machine learning with our comprehensive course, tailored for technology professionals here in Ireland. Delve into regression algorithms like Random Forests and Decision Trees, and get to grips with model evaluation metrics such as RMSE and MAE. Explore data preprocessing techniques, including feature scaling and encoding, to get your data shipshape. Hone your skills with feature selection methods, proper project documentation, and Python libraries like NumPy and Pandas. Optimise your models with hyperparameter tuning and ensemble methods. Sign up now to boost your expertise in machine learning.
- Master regression: Implement Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use RMSE, MAE, and cross-validation for performance metrics.
- Preprocess data: Scale features, handle missing data, and encode categorical variables.
- Optimize models: Apply hyperparameter tuning, ensemble methods, and search strategies.
- Analyse data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights.

from 4 to 360h flexible workload
certificate recognized by the MEC
What will I learn?
Unlock the potential of Python for machine learning with our comprehensive course, tailored for technology professionals here in Ireland. Delve into regression algorithms like Random Forests and Decision Trees, and get to grips with model evaluation metrics such as RMSE and MAE. Explore data preprocessing techniques, including feature scaling and encoding, to get your data shipshape. Hone your skills with feature selection methods, proper project documentation, and Python libraries like NumPy and Pandas. Optimise your models with hyperparameter tuning and ensemble methods. Sign up now to boost your expertise in machine learning.
Elevify advantages
Develop skills
- Master regression: Implement Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use RMSE, MAE, and cross-validation for performance metrics.
- Preprocess data: Scale features, handle missing data, and encode categorical variables.
- Optimize models: Apply hyperparameter tuning, ensemble methods, and search strategies.
- Analyse data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights.
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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