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AI in Healthcare Course
Unlock the potential of AI in healthcare with our comprehensive course, designed specifically for engineering professionals like yourself. Delve into data exploration, mastering techniques such as outlier detection and data distribution analysis. Sharpen your skills with data pre-processing strategies, including normalisation and encoding. Fine-tune models using ensemble methods and hyperparameter tuning. Learn to develop predictive models, select the most relevant features, and evaluate their performance. Enhance your expertise with practical, high-quality, and to-the-point learning.
- Master data exploration: Detect outliers and understand data distributions thoroughly.
- Optimise models: Effectively use ensemble methods and hyperparameter tuning.
- Enhance pre-processing: Normalise data and efficiently encode categorical variables.
- Develop predictive models: Choose appropriate algorithms and apply cross-validation techniques.
- Evaluate model performance: Analyse accuracy, precision, recall, and F1-score.

flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Unlock the potential of AI in healthcare with our comprehensive course, designed specifically for engineering professionals like yourself. Delve into data exploration, mastering techniques such as outlier detection and data distribution analysis. Sharpen your skills with data pre-processing strategies, including normalisation and encoding. Fine-tune models using ensemble methods and hyperparameter tuning. Learn to develop predictive models, select the most relevant features, and evaluate their performance. Enhance your expertise with practical, high-quality, and to-the-point learning.
Elevify advantages
Develop skills
- Master data exploration: Detect outliers and understand data distributions thoroughly.
- Optimise models: Effectively use ensemble methods and hyperparameter tuning.
- Enhance pre-processing: Normalise data and efficiently encode categorical variables.
- Develop predictive models: Choose appropriate algorithms and apply cross-validation techniques.
- Evaluate model performance: Analyse accuracy, precision, recall, and F1-score.
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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