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Duration 35 hours
Course Outline
Introduction to Artificial Intelligence Applications in Python
- Core principles and operational scope of AI technologies
- Essential Python libraries for developing AI solutions for government
- Architectural frameworks and operational workflows for AI projects
Preparation and Processing of Data for AI Systems
- Data cleansing, structural transformation, and feature engineering techniques
- Strategies for managing missing values and class imbalance issues
- Methodologies for feature scaling and data encoding
Supervised Learning Methodologies
- Algorithms for regression and classification tasks
- Ensemble strategies including Random Forest and Gradient Boosting
- Techniques for hyperparameter optimization and cross-validation
Unsupervised Learning Methodologies
- Clustering algorithms such as K-Means, DBSCAN, and hierarchical approaches
- Dimensionality reduction techniques including PCA and t-SNE
- Practical applications of unsupervised learning for government use cases
Neural Networks and Deep Learning Architecture
- Fundamentals of TensorFlow and Keras frameworks
- Construction and training of feedforward neural network models
- Strategies for enhancing neural network performance and reliability
Foundations of Reinforcement Learning
- Core concepts of agents, operational environments, and reward structures
- Implementation of foundational reinforcement learning algorithms
- Strategic applications of reinforcement learning in public sector contexts
Deployment of AI Models in Production Environments
- Protocols for persisting and retrieving trained models
- Integration of AI models into enterprise applications via API interfaces
- Ongoing monitoring and maintenance of AI systems to ensure operational integrity
Conclusion and Recommended Future Actions
Requirements
- Proficiency in core Python programming principles and syntax
- Practical experience with data analysis frameworks such as NumPy and pandas
- Familiarity with fundamental machine learning concepts and algorithmic foundations
Target Audience
- Software developers seeking to enhance their expertise in AI solution development
- Data analysts aiming to apply AI techniques to complex institutional datasets
- Research and Development professionals responsible for engineering AI-powered applications
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
