Course Outline
Introduction to Machine Learning for Government
- Machine learning as a core component of Artificial Intelligence for government operations
- Types of machine learning: supervised, unsupervised, reinforcement, and semi-supervised
- Common ML algorithms used in governmental applications
- Challenges, risks, and potential uses of ML in AI for government agencies
- Overfitting and the bias-variance tradeoff in governmental data analysis
Machine Learning Techniques and Workflow for Government
- The Machine Learning lifecycle: from problem identification to deployment for government projects
- Classification, regression, clustering, and anomaly detection techniques
- When to use supervised versus unsupervised learning in governmental datasets
- Understanding reinforcement learning in automating government processes
- Considerations in ML-driven decision-making for public sector applications
Data Preprocessing and Feature Engineering for Government
- Data preparation: loading, cleaning, and transforming governmental data
- Feature engineering: encoding, transformation, and creation of relevant features for government datasets
- Feature scaling: normalization and standardization techniques for government data
- Dimensionality reduction: Principal Component Analysis (PCA) and variable selection methods
- Exploratory data analysis and visualization of public sector data
Neural Networks and Deep Learning for Government
- Introduction to neural networks and their applications in government operations
- Structure: input, hidden, and output layers in governmental models
- Backpropagation and activation functions in government machine learning
- Neural networks for classification and regression tasks in public sector analytics
- Use of neural networks in forecasting and pattern recognition for government agencies
Sales Forecasting and Predictive Analytics for Government
- Time series vs regression-based forecasting methods for government data
- Decomposing time series: trend, seasonality, and cycles in public sector datasets
- Techniques: linear regression, exponential smoothing, and ARIMA models for government use
- Neural networks for nonlinear forecasting in governmental applications
- Case study: Forecasting monthly sales volume for government contracts
Case Studies in Governmental Applications
- Advanced feature engineering for improved prediction using linear regression in government projects
- Segmentation analysis using clustering and self-organizing maps for public sector data
- Market basket analysis and association rule mining for retail insights applicable to government procurement
- Customer default classification using logistic regression, decision trees, XGBoost, and SVM in governmental financial risk assessment
Summary and Next Steps for Government
Requirements
- A foundational understanding of machine learning principles and their applications for government and other sectors.
- Experience working in spreadsheet environments or with data analysis tools.
- Exposure to Python or another programming language is beneficial but not required.
- An interest in applying machine learning techniques to address real-world business and forecasting challenges for government and industry.
Audience
- Business analysts
- AI professionals
- Data-driven decision makers and managers for government and private organizations
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
