Courses

Neural networks, decision trees and ensemble methods.

Fundamentals of deep learning and neural network architectures.

Introduction to cybersecurity concepts and practices.

Building and training models with Keras and TensorFlow.

Machine learning fundamentals and Python implementation.

Neural networks development with PyTorch framework.

Python programming fundamentals and best practices.

Introduction to prompt engineering for LLMs.

Prompt engineering techniques and applications.

Supervised learning, regression and classification algorithms.

SOC fundamentals and security monitoring.

Clustering, dimensionality reduction and unsupervised methods.

AI coding assistant architecture, tool use systems, context management and GitHub integration.

Building MCP servers and clients from scratch, covering tools, resources and prompts for connecting Claude with external services.

Advanced MCP development covering server-client communication, transport mechanisms, sampling, notifications and production deployment.