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AI in Healthcare Course
Unlock the power of AI in healthcare with our in-depth course designed for engineering professionals. Get stuck in to data exploration, learning the best ways to spot outliers and understand how data is spread out. Improve your abilities with data preparation methods, including normalising and encoding data. Fine-tune models using ensemble methods and hyperparameter tuning. Learn how to build predictive models, select the right features, and assess how well they perform. Boost your knowledge with learning that's practical, top-notch, and to the point.
- Master data exploration: Find outliers and understand data distributions properly.
- Optimize models: Use ensemble methods and hyperparameter tuning effectively to get the best results.
- Enhance preprocessing: Normalize data and encode categorical variables properly and efficiently.
- Develop predictive models: Choose the right algorithms and use cross-validation techniques like a pro.
- Evaluate model performance: Analyse accuracy, precision, recall, and F1-score to see how well your model is doing.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the power of AI in healthcare with our in-depth course designed for engineering professionals. Get stuck in to data exploration, learning the best ways to spot outliers and understand how data is spread out. Improve your abilities with data preparation methods, including normalising and encoding data. Fine-tune models using ensemble methods and hyperparameter tuning. Learn how to build predictive models, select the right features, and assess how well they perform. Boost your knowledge with learning that's practical, top-notch, and to the point.
Elevify advantages
Develop skills
- Master data exploration: Find outliers and understand data distributions properly.
- Optimize models: Use ensemble methods and hyperparameter tuning effectively to get the best results.
- Enhance preprocessing: Normalize data and encode categorical variables properly and efficiently.
- Develop predictive models: Choose the right algorithms and use cross-validation techniques like a pro.
- Evaluate model performance: Analyse accuracy, precision, recall, and F1-score to see how well your model is doing.
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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