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AI Engineering Course
Boost your tech career with our AI Engineering Course, specifically crafted for technology professionals keen to excel in AI. Delve into model evaluation techniques such as Recall, F1-Score, and cross-validation. Refine your documentation abilities to a professional standard and explore machine learning algorithms for predicting customer churn, including decision trees and logistic regression. Gain expertise in data exploration, feature encoding, and model optimisation strategies. Join us to convert data into actionable insights and drive innovation in your domain.
- Master model evaluation: Improve accuracy using metrics like Recall, F1-Score, and Precision.
- Document effectively: Justify decisions and interpret results with clarity and attention to detail.
- Predict churn: Employ Decision Trees, Random Forests, and Logistic Regression techniques.
- Prepare data: Conduct exploratory data analysis and manage missing data efficiently.
- Optimise models: Implement ensemble methods and fine-tune hyperparameters for optimal performance.

flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Boost your tech career with our AI Engineering Course, specifically crafted for technology professionals keen to excel in AI. Delve into model evaluation techniques such as Recall, F1-Score, and cross-validation. Refine your documentation abilities to a professional standard and explore machine learning algorithms for predicting customer churn, including decision trees and logistic regression. Gain expertise in data exploration, feature encoding, and model optimisation strategies. Join us to convert data into actionable insights and drive innovation in your domain.
Elevify advantages
Develop skills
- Master model evaluation: Improve accuracy using metrics like Recall, F1-Score, and Precision.
- Document effectively: Justify decisions and interpret results with clarity and attention to detail.
- Predict churn: Employ Decision Trees, Random Forests, and Logistic Regression techniques.
- Prepare data: Conduct exploratory data analysis and manage missing data efficiently.
- Optimise models: Implement ensemble methods and fine-tune hyperparameters for optimal performance.
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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