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Machine Learning Course
Unlock the capabilities of machine learning with our detailed course crafted for technology professionals. Delve into data collection and exploration utilising Pandas and NumPy, become proficient in dataset splitting strategies, and assess models with accuracy. Learn crucial preprocessing techniques, explore advanced model selection, and gain understanding of regression models such as Decision Trees and Random Forests. Enhance your skills with practical, top-notch content ensuring you're prepared to handle real-world challenges. Enrol now and transform your career!
- Become proficient in data handling: Load, inspect, and select datasets using Pandas and NumPy.
- Implement dataset splitting: Apply cross-validation and stratified sampling techniques.
- Evaluate model performance: Understand MAE, MSE, and R-squared metrics.
- Preprocess data effectively: Manage missing values and encode categorical variables.
- Optimise models: Tune hyperparameters and utilise ensemble methods.

flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Unlock the capabilities of machine learning with our detailed course crafted for technology professionals. Delve into data collection and exploration utilising Pandas and NumPy, become proficient in dataset splitting strategies, and assess models with accuracy. Learn crucial preprocessing techniques, explore advanced model selection, and gain understanding of regression models such as Decision Trees and Random Forests. Enhance your skills with practical, top-notch content ensuring you're prepared to handle real-world challenges. Enrol now and transform your career!
Elevify advantages
Develop skills
- Become proficient in data handling: Load, inspect, and select datasets using Pandas and NumPy.
- Implement dataset splitting: Apply cross-validation and stratified sampling techniques.
- Evaluate model performance: Understand MAE, MSE, and R-squared metrics.
- Preprocess data effectively: Manage missing values and encode categorical variables.
- Optimise models: Tune hyperparameters and utilise ensemble methods.
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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