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Machine Learning Course
Unlock the power of machine learning with our comprehensive course designed for technology professionals. Dive into data collection and exploration using Pandas and NumPy, master dataset splitting strategies, and evaluate models with precision. Learn essential pre-processing techniques, explore advanced model selection, and gain insights into regression models like Decision Trees and Random Forests. Enhance your skills with practical, high-quality content that ensures you're ready to tackle real-world challenges. Join now and transform your career!
- Master data handling: Load, inspect, and select datasets with Pandas and NumPy.
- Implement dataset splitting: Apply cross-validation and stratified sampling techniques.
- Evaluate model performance: Understand MAE, MSE, and R-squared metrics.
- Pre-process data effectively: Handle missing values and encode categorical variables.
- Optimise models: Tune hyperparameters and utilise ensemble methods.

4 to 360 hours flexible workload
certificate recognised by MEC
What will I learn?
Unlock the power of machine learning with our comprehensive course designed for technology professionals. Dive into data collection and exploration using Pandas and NumPy, master dataset splitting strategies, and evaluate models with precision. Learn essential pre-processing techniques, explore advanced model selection, and gain insights into regression models like Decision Trees and Random Forests. Enhance your skills with practical, high-quality content that ensures you're ready to tackle real-world challenges. Join now and transform your career!
Elevify advantages
Develop skills
- Master data handling: Load, inspect, and select datasets with Pandas and NumPy.
- Implement dataset splitting: Apply cross-validation and stratified sampling techniques.
- Evaluate model performance: Understand MAE, MSE, and R-squared metrics.
- Pre-process data effectively: Handle missing values and encode categorical variables.
- Optimise models: Tune hyperparameters and utilise ensemble methods.
Suggested summary
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