Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Machine Learning for Government
- Introduction to machine learning concepts and workflows for government
- Supervised vs. unsupervised learning in the context of public sector data
- Evaluating machine learning models: metrics and techniques tailored for government applications
Bayesian Methods for Government
- Naive Bayes and multinomial models for governmental datasets
- Bayesian categorical data analysis for public sector insights
- Bayesian graphical models to enhance decision-making processes in government
Regression Techniques for Government
- Linear regression for analyzing government data
- Logistic regression for predictive modeling in public services
- Generalized Linear Models (GLM) to address complex government datasets
- Mixed models and additive models for comprehensive analysis of governmental information
Dimensionality Reduction for Government
- Principal Component Analysis (PCA) to simplify high-dimensional data in the public sector
- Factor Analysis (FA) for identifying underlying factors in government datasets
- Independent Component Analysis (ICA) for isolating independent sources of information in public data
Classification Methods for Government
- K-Nearest Neighbors (KNN) for classification tasks in government applications
- Support Vector Machines (SVM) for regression and classification in governmental contexts
- Boosting and ensemble models to improve predictive accuracy in public sector analytics
Neural Networks for Government
- Introduction to neural networks for government use cases
- Applications of deep learning in classification and regression tasks for governmental data
- Training and tuning neural networks to optimize performance for government datasets
Advanced Algorithms and Models for Government
- Hidden Markov Models (HMM) for sequence analysis in public sector applications
- State Space Models for dynamic data analysis in government
- EM Algorithm to handle missing data in governmental datasets
Clustering Techniques for Government
- Introduction to clustering and unsupervised learning for government data
- Popular clustering algorithms: K-Means, Hierarchical Clustering for public sector use
- Use cases and practical applications of clustering in governmental workflows
Summary and Next Steps for Government
Requirements
- Basic understanding of statistics and data analysis for government
- Programming experience in R, Python, or other relevant programming languages
Audience
- Data scientists
- Statisticians
14 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
