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
Testimonials (5)
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
it was informative and useful
Brenton - Lotterywest
Course - Building Web Applications in R with Shiny
Many examples and exercises related to the topic of the training.
Tomasz - Ministerstwo Zdrowia
Course - Advanced R Programming
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
