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Hands-on, industry-designed learning programs to build job-ready skills through AI-powered interactive lessons.
Build Applications Using Claude Code
An intensive 4-week bootcamp designed to transform junior developers into AI-augmented full-stack developers capable of building, debugging, and deploying production-ready web applications using Claude Code. Participants will master terminal-based workflows, natural language prompting for code generation, Git version control, and full-stack development (React/Node.js) while leveraging Claude Code as their primary coding assistant. By program completion, learners will have deployed a portfolio-quality capstone application and demonstrated job-ready proficiency in the emerging agentic coding paradigm.
- Execute 30+ bash/PowerShell commands to navigate, search, and manipulate files in multi-directory projects and debug common terminal errors using Claude Code
- Perform Git operations (clone, branch, commit, merge, PR) exclusively via CLI, resolve merge conflicts in 10+ file codebases, and author CLAUDE.md files that enable accurate AI queries in 1000+ LOC repositories
- Craft context-rich prompts using XML tagging and file references that produce production-quality code, and chain 5+ prompts to build full features with minimal manual editing
- Generate functional React frontends with routing and state management, build Node.js/Express backends with RESTful endpoints and MongoDB persistence, and integrate frontend and backend into cohesive full-stack applications with authentication
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Deploying Production-Ready LLM & Agent Systems
An 8-week intensive program that prepares junior developers to architect, build, and deploy enterprise-grade LLM applications with confidence. Progress from foundational prompt engineering to building multi-agent systems with full observability, deployment pipelines, and cost optimization strategies. By program completion, graduates will deliver a portfolio demonstrating production-ready RAG systems, agentic workflows, and LLMOps practices that directly address the 73% hiring gap for deployment-capable junior developers.
- Construct zero-shot, few-shot, and chain-of-thought prompts that consistently generate structured outputs (JSON, tables) with 90%+ accuracy
- Build end-to-end RAG pipelines with advanced retrieval strategies (hybrid search, query expansion, reranking) achieving 85%+ answer accuracy on production test sets
- Design and deploy multi-agent systems with 3+ specialized agents using LangGraph for state management and inter-agent coordination with 70%+ task completion rates
Firas Bayram
Machine Learning from Scratch: The Systems View (Advanced)
A 6-week intensive program designed to transform ML practitioners into ML Systems Engineers by building a complete mini-PyTorch framework from first principles. Learners will implement automatic differentiation, neural network layers, optimizers, and GPU acceleration basics while understanding the computational and numerical considerations that drive production ML systems. By coupling each implementation with performance optimization and framework comparisons, students develop the deep systems knowledge required for mid-level engineering roles where architectural decisions and production trade-offs are daily responsibilities.
- Implement a complete automatic differentiation engine supporting both scalar and tensor operations with dynamic computational graph construction
- Build neural network primitives (linear layers, activations, batch normalization) from scratch with proper initialization strategies to prevent vanishing/exploding gradients
- Code and analyze gradient-based optimization algorithms (SGD, Momentum, Adam) understanding their convergence properties and hyperparameter sensitivities
Firas Bayram
Machine Learning from Scratch: The Systems View (Junior Edition)
A 2-month intensive program designed to prepare aspiring ML Systems Engineers for junior-level roles by building strong mathematical intuition, core ML understanding, and systematic thinking about production systems. Students will master the mathematical foundations (linear algebra, calculus, probability), understand essential ML concepts (loss functions, optimization, regularization), and progressively implement systems using Python, scikit-learn, and PyTorch. The program emphasizes conceptual clarity before coding, preparing learners to design, implement, and reason about ML systems that work in production environments.
- Apply linear algebra operations (matrix multiplication, decomposition, eigenvalues) to understand data transformations and dimensionality reduction in ML contexts
- Compute gradients and partial derivatives to explain how optimization algorithms update model parameters
- Use probability distributions and Bayes' rule to model uncertainty and interpret probabilistic predictions
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