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Python For Machine Learning Course
Unlock the power of Python for machine learning with our comprehensive course designed for technology professionals. Dive into regression algorithms such as Random Forests and Decision Trees, master model evaluation metrics such as RMSE and MAE, and explore data preprocessing techniques including feature scaling and encoding. Enhance your skills with feature selection methods, project documentation, and Python libraries like NumPy and Pandas. Optimise models with hyperparameter tuning and ensemble methods. Join now to elevate 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.
- Optimise models: Apply hyperparameter tuning, ensemble methods, and search strategies.
- Analyse data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights.
4 to 360 hours flexible workload
certificate recognised by MEC
What will I learn?
Unlock the power of Python for machine learning with our comprehensive course designed for technology professionals. Dive into regression algorithms such as Random Forests and Decision Trees, master model evaluation metrics such as RMSE and MAE, and explore data preprocessing techniques including feature scaling and encoding. Enhance your skills with feature selection methods, project documentation, and Python libraries like NumPy and Pandas. Optimise models with hyperparameter tuning and ensemble methods. Join now to elevate 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.
- Optimise models: Apply hyperparameter tuning, ensemble methods, and search strategies.
- Analyse data: Utilize NumPy, Pandas, Matplotlib, and Seaborn for data insights.
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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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