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AI And Machine Learning Course
Give your Business Intelligence career a boost with our AI and Machine Learning Course. Get to grips with data preparation, including formatting, dealing with missing values, and outlier treatment. Delve into predictive algorithms like linear regression, decision trees, and more advanced techniques such as random forests. Fine-tune models using ensemble methods and hyperparameter tuning. Learn about cross-validation, data splitting, and performance metrics. Sharpen your skills in interpreting results and writing effective reports. Sign up now for focused, top-notch learning geared towards BI professionals.
- Get to grips with data cleaning: Deal with missing values and outliers with precision.
- Put ML algorithms into practice: Apply linear regression and decision trees effectively.
- Fine-tune models: Improve performance with hyperparameter tuning and ensemble methods.
- Assess model accuracy: Use cross-validation and performance metrics like MAE and RMSE.
- Report insights: Put together compelling reports and confidently analyse model predictions.

from 4 to 360h flexible workload
certificate recognized by the MEC
What will I learn?
Give your Business Intelligence career a boost with our AI and Machine Learning Course. Get to grips with data preparation, including formatting, dealing with missing values, and outlier treatment. Delve into predictive algorithms like linear regression, decision trees, and more advanced techniques such as random forests. Fine-tune models using ensemble methods and hyperparameter tuning. Learn about cross-validation, data splitting, and performance metrics. Sharpen your skills in interpreting results and writing effective reports. Sign up now for focused, top-notch learning geared towards BI professionals.
Elevify advantages
Develop skills
- Get to grips with data cleaning: Deal with missing values and outliers with precision.
- Put ML algorithms into practice: Apply linear regression and decision trees effectively.
- Fine-tune models: Improve performance with hyperparameter tuning and ensemble methods.
- Assess model accuracy: Use cross-validation and performance metrics like MAE and RMSE.
- Report insights: Put together compelling reports and confidently analyse model predictions.
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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