Education

Introduction to Claude Tag

Covered Claude Tag: bringing Claude into team workflows and collaborating with it where the work happens.

Building effective human-agent teams

Covered how to design effective collaboration between people and AI agents as a team.

Deploying Claude Enterprise with Confidence

Covered the five decisions that shape a Claude Enterprise rollout: planning, configuring and adopting Claude across an organization.

Model Context Protocol: Advanced Topics

Covered advanced Model Context Protocol topics with Anthropic Claude: building more capable MCP servers and clients and handling complex tool and context scenarios in production agents.

Building with the Claude API

Built production-ready applications with the Claude API: messages, tool use, streaming, and patterns for reliable integration in real workflows.

Introduction to Model Context Protocol

Covered Model Context Protocol (MCP) fundamentals with Anthropic Claude: connecting tools and data sources to agents, structuring context exchange, and building practical MCP integrations.

Introduction to subagents

Covered subagent fundamentals with Anthropic Claude: designing specialized agents, coordinating multi-agent workflows, and applying AI agent patterns in practical development scenarios.

Introduction to agent skills

Covered how to build and use Claude Skills: structuring reusable agent capabilities, integrating them into workflows, and applying them effectively in production-oriented scenarios.

Claude Code 101

Covered Claude Code fundamentals: setup, core workflows, and practical use of Anthropic's agentic coding environment for day-to-day development tasks.

Claude 101

Covered Claude fundamentals: capabilities, prompting, safe and effective use, and how to get strong results from the product.

Claude Code in Action

Covered applying Claude Code in real workflows: agentic coding patterns, tooling integration, and shipping reliable changes with AI-assisted development.

Deep Agents with LangGraph

Built deep agents with LangGraph for complex, long-running tasks, including to-do lists, file management, and sub-agent creation.

Deep Research with LangGraph

Built a capstone deep research workflow with LangGraph: planning, web search, citations, and iterative refinement.

Building Ambient Agents with LangGraph

Built ambient agents with LangGraph: state graphs, tools, memory, and multi-agent patterns.

Foundation: Introduction to LangGraph

Covered LangGraph fundamentals: state and memory, human-in-the-loop workflows, long-term persistence, and deployment of reliable agents.

Iterative Tools for Data Scientists and Analysts

Covered data version control, model training, and model evaluation. Used DVC/MLflow for workflow automation.

Fundamentals of Deep Learning

Covered neural networks, backpropagation, and optimization techniques. Implemented CNNs and RNNs in TensorFlow/Keras.

Introduction to Neural Networks and PyTorch

Built neural networks with PyTorch, covering CNNs, RNNs, backpropagation, and activation functions. Applied to image classification and sequence modeling.

Object-Oriented Data Structures in C++

Implemented OOP data structures (linked lists, trees, hash tables) with complexity analysis in C++. Focused on memory management and recursive algorithms.

Applied AI with Deep Learning

Covered deep learning models (GANs, LSTMs, autoencoders) using TensorFlow/Keras, including model deployment via Flask APIs.

Ordered Data Structures

Built hierarchical structures (heaps, B-trees) and graph algorithms (Dijkstra, BFS/DFS). Optimized memory allocation patterns.