Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Artificial Intelligence Using Python
- Core principles and operational boundaries of AI
- Python frameworks for AI implementation
- Architectural design and procedural workflows for AI projects
Data Curation and Preprocessing for AI
- Data sanitization, structural transformation, and feature development
- Strategies for managing incomplete and imbalanced datasets
- Feature normalization and categorical encoding
Supervised Learning Methodologies
- Regression and classification models
- Ensemble strategies: Random Forest and Gradient Boosting
- Hyperparameter optimization and cross-validation procedures
Unsupervised Learning Methodologies
- Clustering techniques: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction: PCA and t-SNE
- Operational use cases for unsupervised learning
Neural Networks and Deep Learning
- Overview of TensorFlow and Keras
- Construction and training of feedforward neural networks
- Optimization of neural network efficacy
Reinforcement Learning (Introduction)
- Fundamental concepts of agents, environments, and reward signals
- Implementation of basic reinforcement learning algorithms
- Applications of reinforcement learning for government
Deployment of AI Models
- Persistence and retrieval of trained models
- Integration of models into enterprise applications via APIs
- Ongoing monitoring and maintenance of AI systems in production environments
Summary and Subsequent Actions
Requirements
- Proficient understanding of Python programming fundamentals
- Practical experience with data analysis libraries such as NumPy and pandas
- Foundational knowledge of machine learning concepts and algorithms
Target Audience
- Software developers seeking to enhance their AI development competencies
- Data analysts aiming to apply AI techniques for government to complex datasets
- R&D professionals engaged in the development of AI-driven applications
35 Hours
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
