Career Advancement Programme in Machine Learning for Claims Reserving
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Course details
• Introduction to Machine Learning: Basic concepts, types of machine learning, and its applications in insurance.
• Data Preprocessing for Machine Learning: Data cleaning, data transformation, feature engineering, and data splitting.
• Supervised Learning Algorithms: Linear regression, logistic regression, decision trees, random forests, and support vector machines.
• Unsupervised Learning Algorithms: K-means clustering, hierarchical clustering, and principal component analysis.
• Model Evaluation Metrics: Mean squared error, mean absolute error, R-squared, precision, recall, F1-score, and ROC curve.
• Time Series Analysis: Autoregressive integrated moving average (ARIMA) models, exponential smoothing, and seasonality analysis.
• Machine Learning for Claims Reserving: Overview of claims reserving, traditional methods, and machine learning applications.
• Implementing Machine Learning Models using Python: Scikit-learn library, data manipulation with Pandas, and data visualization with Matplotlib.
• Deploying Machine Learning Models in Production: Model deployment options, monitoring, and maintenance.
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Career path
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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