Specialized AI Training Programs
Three focused programs addressing specific areas of modern AI practice through implementation-centered learning.
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Our Methodology
Each program follows a consistent approach emphasizing practical implementation over theoretical coverage. Participants work with real datasets and production-grade tools throughout.
Foundation Week
Environment setup, core concepts review, and first working implementation
Core Techniques
Main approaches and architectures with hands-on implementation
Advanced Topics
Specialized techniques and optimization strategies
Project Work
Apply techniques to chosen dataset with instructor guidance
Graph Neural Networks
When data is naturally structured as networks, specialized approaches can capture relational patterns that traditional methods miss. This program introduces graph-based deep learning for applications including social network analysis, molecular property prediction, and recommendation systems. Participants learn to represent, process, and learn from graph-structured data using PyTorch Geometric.
Program Coverage:
- Graph representation learning fundamentals
- Message passing architectures (GCN, GAT, GraphSAGE)
- Node, edge, and graph-level prediction tasks
- Scaling to large graphs
Anomaly Detection Systems
Identifying unusual patterns is valuable across many domains, from fraud prevention to system monitoring. This program develops practical skills in building detection systems using various approaches. Topics include statistical methods, isolation forests, autoencoders, and time-series anomaly detection. Participants work with datasets from security, manufacturing, and financial contexts.
Program Coverage:
- Statistical approaches and baseline methods
- Machine learning techniques (Isolation Forest, One-Class SVM)
- Deep learning approaches (autoencoders, VAEs)
- Time-series specific methods
Federated Learning & Privacy-Preserving AI
As privacy concerns grow, techniques for learning from distributed data without centralizing it become increasingly relevant. This advanced program explores federated learning architectures, differential privacy, secure aggregation, and related approaches. Participants implement privacy-preserving training pipelines and evaluate privacy-utility tradeoffs. The curriculum addresses both technical implementation and regulatory contexts.
Program Coverage:
- Federated learning architectures and communication protocols
- Differential privacy mechanisms
- Secure aggregation techniques
- Privacy-utility tradeoff evaluation
Program Comparison
| Feature | Graph Neural Networks | Anomaly Detection | Federated Learning |
|---|---|---|---|
| Duration | 8 weeks | 6 weeks | 10 weeks |
| Investment | SGD 850 | SGD 680 | SGD 1,380 |
| Prerequisites | Neural networks, Python | Data analysis, Python | ML experience, cryptography basics |
| Difficulty | Intermediate | Beginner-Intermediate | Advanced |
| Primary Framework | PyTorch Geometric | scikit-learn, PyTorch | PySyft, TensorFlow |
| Best For | Network data problems | Monitoring & quality systems | Privacy-sensitive applications |
Technical Standards
Code Quality
All implementations follow industry best practices for version control, documentation, and testing. Participants learn to write maintainable code suitable for production deployment.
Performance Focus
Programs address computational efficiency, memory management, and scaling considerations. Participants profile implementations and learn optimization techniques.
Ongoing Support
Instructors remain accessible after program completion. Alumni can return with implementation questions as they apply techniques to professional work.
Select Your Program
Connect with us to discuss which program aligns with your technical background and learning objectives.