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Machine Learning Course
Open up the power of Machine Learning with our full course made for tech people like you. Jjula inside data collection and exploration using Pandas and NumPy, be a master at dividing your dataset, and grade models with properness. Learn important ways of preparing your data, explore advanced model selection, and get deep knowledge on regression models like Decision Trees and Random Forests. Improve your skills with content that is practical and of high quality so that you are ready to handle challenges from the real world. Join now and change your career!
- Be a master at handling data: 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.
- Preprocess data effectively: Handle missing values and encode categorical variables.
- Optimize models: Tune hyperparameters and utilize ensemble methods.

flexible workload from 4 to 360h
certificate recognized by MEC
What will I learn?
Open up the power of Machine Learning with our full course made for tech people like you. Jjula inside data collection and exploration using Pandas and NumPy, be a master at dividing your dataset, and grade models with properness. Learn important ways of preparing your data, explore advanced model selection, and get deep knowledge on regression models like Decision Trees and Random Forests. Improve your skills with content that is practical and of high quality so that you are ready to handle challenges from the real world. Join now and change your career!
Elevify advantages
Develop skills
- Be a master at handling data: 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.
- Preprocess data effectively: Handle missing values and encode categorical variables.
- Optimize models: Tune hyperparameters and utilize ensemble methods.
Suggested summary
Before starting, you can change the chapters and 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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