Course Outline
Overview of AI for Government in Python
- Key concepts and scope of artificial intelligence (AI)
- Python libraries essential for AI development
- Structuring and managing AI projects and workflows for government
Data Preparation for AI for Government
- Data cleaning, transformation, and feature engineering techniques
- Strategies for handling missing and unbalanced data in public sector datasets
- Methods for feature scaling and encoding to enhance model performance
Supervised Learning Techniques for Government
- Regression and classification algorithms tailored for government applications
- Ensemble methods such as Random Forest and Gradient Boosting, optimized for public sector data
- Techniques for hyperparameter tuning and cross-validation to ensure robust model validation
Unsupervised Learning Techniques for Government
- Clustering methods including K-Means, DBSCAN, and hierarchical clustering, suitable for government datasets
- Dimensionality reduction techniques like PCA and t-SNE to manage complex data structures
- Use cases for unsupervised learning in public sector applications
Neural Networks and Deep Learning for Government
- Introduction to TensorFlow and Keras, with a focus on government-specific use cases
- Building and training feedforward neural networks for enhanced data analysis
- Optimizing neural network performance to meet the high standards of public sector projects
Reinforcement Learning (Intro) for Government
- Core concepts of agents, environments, and rewards in reinforcement learning, adapted for government applications
- Implementing basic reinforcement learning algorithms to solve complex problems in the public sector
- Applications of reinforcement learning in various government domains
Deploying AI Models for Government
- Saving and loading trained models to ensure seamless integration into existing systems
- Integrating AI models into applications via APIs to enhance public sector workflows
- Monitoring and maintaining AI systems in production to ensure ongoing performance and compliance with government standards
Summary and Next Steps for Government
Requirements
- Solid understanding of Python programming fundamentals
- Experience with data analysis libraries, such as NumPy and pandas
- Basic knowledge of machine learning concepts and algorithms
Audience
- Software developers looking to enhance their AI development capabilities for government projects
- Data analysts aiming to apply AI techniques to complex datasets in public sector contexts
- R&D professionals developing AI-powered applications for government use
Testimonials (3)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
