Course Outline
Overview of Predictive Artificial Intelligence
- Definition and scope of predictive AI for government applications
- Historical development and progression of predictive analytics
- Fundamental concepts of machine learning and data mining techniques
Data Acquisition and Preprocessing
- Identification and acquisition of relevant datasets
- Data cleansing and preparation for analytical processing
- Classification of data types and assessment of source reliability
Exploratory Data Analysis (EDA)
- Utilization of visualization tools to derive actionable insights
- Application of descriptive statistics and data summarization methods
- Detection of patterns and correlations within structured data
Statistical Modeling Frameworks
- Principles of statistical inference in decision-making
- Application of regression analysis techniques
- Implementation of classification models for categorical outcomes
Machine Learning Algorithms for Forecasting
- Survey of supervised learning algorithms suitable for public sector use
- Functionality of decision trees and random forest methodologies
- Fundamentals of neural networks and deep learning architectures
Model Evaluation and Selection Criteria
- Assessment of model accuracy and performance metrics for accountability
- Implementation of cross-validation techniques to ensure robustness
- Management of overfitting through hyperparameter tuning
Practical Applications of Predictive AI in Governance
- Analysis of case studies demonstrating efficacy across government sectors
- Ethical considerations and bias mitigation in predictive modeling
- Examination of limitations and operational challenges in deploying Predictive AI for government initiatives
Applied Capstone Project
- Construction of a predictive model using authentic agency datasets
- Deployment of the model to generate forecasted outcomes
- Evaluation and interpretation of results to inform policy decisions
Conclusion and Strategic Next Steps
Requirements
- Foundational knowledge of statistical principles
- Proficiency in at least one programming language
- Competency in data management and spreadsheet applications
- No previous background in artificial intelligence or data science is necessary
Audience
- Information technology professionals
- Data analysts
- Technical personnel
Testimonials (3)
basics and loved the prepared documents and exercises
Rekha Nallam - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
Opportunity to use a pre-created models, understand how do they work and tweak them live and see the results. Choice ov VSCode with Jupyter was a perfect option for such way of leading the training.
Krzysztof - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
Difficult topics presented in simple, user-friendly way
