Course Outline
Introduction to Machine Learning for Government
- Machine learning as a core component of Artificial Intelligence
- Types of machine learning: supervised, unsupervised, reinforcement, semi-supervised
- Common ML algorithms used in government applications
- Challenges, risks, and potential uses of ML in AI for government
- Overfitting and the bias-variance tradeoff
Machine Learning Techniques and Workflow for Government
- The Machine Learning lifecycle: from problem identification to deployment
- Classification, regression, clustering, anomaly detection
- When to use supervised vs unsupervised learning in government contexts
- Understanding reinforcement learning for automation in public sector workflows
- Considerations in ML-driven decision-making for government
Data Preprocessing and Feature Engineering for Government
- Data preparation: loading, cleaning, transforming data for government use
- Feature engineering: encoding, transformation, creation to enhance public sector analytics
- Feature scaling: normalization, standardization for consistent data analysis
- Dimensionality reduction: PCA, variable selection to optimize data models
- Exploratory data analysis and business data visualization for government insights
Case Studies in Government Applications
- Advanced feature engineering for improved prediction using linear regression in public sector projects
- Time series analysis and forecasting monthly volume of sales: seasonal adjustment, regression, exponential smoothing, ARIMA, neural networks for government agencies
- Segmentation analysis using clustering and self-organizing maps for targeted policy implementation
- Market basket analysis and association rule mining for retail insights in public procurement
- Customer default classification using logistic regression, decision trees, XGBoost, SVM for government financial risk management
Summary and Next Steps for Government
Requirements
- Basic understanding of machine learning concepts and terminology for government use
- Familiarity with data analysis or working with datasets in a public sector context
- Some exposure to a programming language (e.g., Python) is beneficial but not mandatory for government professionals
Audience
- Business analysts and data professionals within the public sector
- Decision makers interested in AI adoption for government operations
- IT professionals exploring machine learning applications for government services
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
