Introduction to Predictive AI Training Course

Introduction

• Defining Predictive Artificial Intelligence (AI) for government operations
• Historical context and evolution of predictive analytics in the public sector
• Basic principles of machine learning and data mining for government applications

Data Collection and Preprocessing

• Gathering relevant data for government initiatives
• Cleaning and preparing data for analysis in public sector projects
• Understanding data types and sources used in government datasets

Exploratory Data Analysis (EDA)

• Visualizing data to gain insights for government decision-making
• Descriptive statistics and data summarization techniques for public sector use
• Identifying patterns and relationships in government data

Statistical Modeling

• Basics of statistical inference for government applications
• Regression analysis to support public sector predictions
• Classification models for government data categorization

Machine Learning Algorithms for Prediction

• Overview of supervised learning algorithms for government use
• Decision trees and random forests in public sector predictive modeling
• Neural networks and deep learning basics for government applications

Model Evaluation and Selection

• Understanding model accuracy and performance metrics for government projects
• Cross-validation techniques to ensure robust models in the public sector
• Addressing overfitting and model tuning in government predictive analytics

Practical Applications of Predictive AI

• Case studies across various industries, with a focus on government applications
• Ethical considerations in predictive modeling for government operations
• Limitations and challenges of Predictive AI in the public sector

Hands-On Project

• Working with a dataset to create a predictive model for government use
• Applying the model to make predictions relevant to government operations
• Evaluating and interpreting the results for government decision-making

Summary and Next Steps