Learn. Build. Ship.
From Getting Started to Deep Dive.

Understand how AI actually works — LLMs, tokens, embeddings, the OpenAI API — then build your first AI-powered app. No ML degree required.

Go from basic prompts to advanced techniques — few-shot, chain-of-thought, ReAct, structured output, and building reliable AI pipelines.

A complete practical path from Python and Kaggle fundamentals to machine learning, NLP, transformers, multilingual assistants, and beginner research workflow.

Build a production pharmacist chatbot from scratch — covering every skill that matters for an AI systems engineer in 2026: AI agents, RAG pipelines, practical LLM integration, FastAPI backend, prompt engineering, vector search, Azure cloud, security & privacy, team collaboration, and production delivery. One real project. Ten real skills. Fully deployed.

Master Angular from environment setup to production-ready apps. Covers components, directives, forms, routing, HTTP, NgRx state management, performance, testing, and full interview prep.

Go from JavaScript to fully typed TypeScript — types, interfaces, generics, utility types, type narrowing, and TypeScript with React in production.

Build full-stack apps with Next.js App Router — Server Components, data fetching, API routes, auth, deployment to Vercel and Azure.

Build production-grade REST APIs in ASP.NET Core from first principles. Covers HTTP fundamentals, routing, status codes, versioning, authentication, content negotiation, caching, file handling, long-running operations, HATEOAS, and end-to-end integration testing.

Build production REST APIs with Node.js and Express — TypeScript, Prisma ORM, JWT authentication, validation, error handling, testing, and deployment. From first server to production-grade API.

Master Python from scratch to production — environments, OOP, file handling, APIs, Pandas, async, automation, and FastAPI. Every skill used in real pipelines, tooling, and framework development.

Master the algorithms and data structures asked in every tech interview. Big O analysis, arrays, trees, graphs, and dynamic programming — with TypeScript solutions for 50+ classic problems.

Go beyond basic SQL — master PostgreSQL indexing, EXPLAIN ANALYZE, window functions, JSONB, partitioning, row-level security, and pgvector for AI applications.

Master every SQL skill needed for data engineering roles — JOINs, aggregations, CTEs, window functions, query optimisation, data transformation, and 300 interview-ready questions.

Build production data pipelines end-to-end — ETL vs ELT, batch vs streaming, medallion architecture, Apache Airflow orchestration, pipeline design patterns, data contracts, and production monitoring.

Master dbt from fundamentals to production — models, sources, refs, materializations, testing, documentation, Jinja macros, snapshots, CI/CD deployment, and Airflow integration.

Master the data modelling skills that separate senior data engineers from juniors — dimensional modelling, star schemas, fact and dimension tables, SCDs, and Data Vault 2.0.

Master professional Git workflows and GitHub Actions CI/CD — branching strategy, pull requests, code reviews, merge vs rebase, conflict resolution, and automated pipelines for Python and dbt.

Use AI tools productively as a data engineer — GitHub Copilot patterns, prompt engineering for code generation, debugging with AI, AI-assisted code reviews, and the workflows that make AI a genuine force multiplier.

Master every major database used in production — PostgreSQL, MySQL, SQL Server, MongoDB, Redis, Cassandra, DynamoDB, and cloud-managed services on Azure, AWS, and GCP. Learn when to use SQL vs NoSQL, how to design schemas, optimize queries, and pick the right database for every access pattern.

Deploy and run production apps on Azure — App Service, Azure Functions, Cosmos DB, Service Bus, Azure AD, GitHub Actions CI/CD, and cost management.

Understand how attacks work so you can defend against them. OWASP Top 10, SQL injection, XSS, CSRF, JWT attacks, authentication, secrets management, threat modelling, and penetration testing basics.

Architect scalable distributed systems. From monolith to microservices — covers system design fundamentals, microservices patterns, DDD, distributed systems, and full interview preparation.

Build and ship production serverless apps on AWS — Python Lambda backend, API Gateway, DynamoDB, React/TypeScript frontend with Amplify, and Cognito auth.

Automate and scale cloud contact centers — provision phone systems on Amazon Connect, build IVR flows, integrate DeepGram AI transcription, and ship real-time call analytics dashboards.

Manage AWS infrastructure as code with Terraform — provision serverless stacks, enforce multi-environment strategy, and automate deployments with GitHub Actions CI/CD.

Build compliant healthcare software on AWS — HIPAA controls, PHI encryption, FHIR data models, headless EHR with Oystehr, and Supabase for authentication and real-time data.

Go from Java fundamentals to production-ready Spring Boot APIs — REST controllers, Spring Data JPA, Spring Security with JWT, testing, and Docker deployment.

Master Vue 3 from first component to production SPA — Composition API, Pinia, Vue Router 4, TypeScript integration, composables, testing with Vitest, and performance optimization.

Master every major database — PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, Cassandra, ClickHouse, Elasticsearch, and more. Know when to use each and why.

Understand Linux deeply — filesystem, processes, permissions, networking, package management, shell scripting, and systemd. Essential knowledge for every backend developer and DevOps engineer.

Learn to build web pages from scratch — HTML structure, CSS styling, Flexbox, Grid, responsive design, and publishing your first site.

Start programming with the language of the web — variables, functions, arrays, objects, DOM manipulation, events, and async/await.

Learn Python from scratch — the most beginner-friendly programming language used for web, AI, data science, and automation.

Learn SQL from scratch — the universal language for querying databases. SELECT, WHERE, JOIN, GROUP BY, and real-world database design.

Learn Java — one of the world's most-used languages for enterprise software, Android, and backend systems. OOP, types, and real code from day one.

Learn C# — Microsoft's powerful, modern language for web APIs, desktop apps, and game development with Unity. Strongly typed, expressive, and professional.

Learn C — the language that built Unix, Linux, Python, and nearly every OS kernel. Understand memory, pointers, and how computers really work.

Learn C++ — the high-performance language powering game engines, trading systems, and embedded software. OOP, templates, STL, and modern C++.

Go from AI curiosity to AI-powered productivity. Learn to use LLMs effectively for coding, writing, research, and building AI-enhanced workflows.

Build autonomous AI agents that plan, use tools, and complete multi-step tasks. MCP, Claude Code agents, LangGraph, and production deployment patterns.

Use AI as your coding co-pilot — Claude Code, Cursor, Copilot. Build faster, debug smarter, and learn to direct AI to write production-quality code.

Bring software engineering discipline to machine learning — track experiments, compare runs, version models, and serve predictions via REST API. Covers Azure ML and Databricks integration.

500,000+ pre-trained models, fine-tuning with the Trainer API, parameter-efficient fine-tuning with LoRA/QLoRA, sentence embeddings for RAG, and production deployment with Inference Endpoints.

Deploy GPT-4o and embeddings on Azure with enterprise security — Managed Identity auth, Private Endpoints, chat completions, function calling, structured output, RAG with pgvector, and cost management.

Build production data pipelines on Databricks — Delta Lake ACID transactions and MERGE upserts, Medallion Architecture, Delta Live Tables, Auto Loader, Structured Streaming, advanced PySpark optimizations, MLflow model lifecycle, Unity Catalog governance, and Feature Store.

Master Snowflake's cloud data platform — virtual warehouses, time travel, zero-copy cloning, dynamic tables, Snowpark Python, data sharing, Streams and Tasks, and cost management.

Build production Power BI solutions — star schema design, DAX measures from basics to time intelligence, DirectQuery vs Import, row-level security, Power BI Embedded in .NET apps, and deployment pipelines.

Production Kubernetes from first principles — workloads (Deployments, StatefulSets, Jobs), networking (Services, Ingress, NetworkPolicy), RBAC, autoscaling (HPA, KEDA, VPA), resource management, Helm, and production readiness.

Production GCP — Cloud Run serverless containers, BigQuery analytics at scale, Cloud SQL, Pub/Sub messaging, Dataflow pipelines, Vertex AI and Gemini, IAM, and Cloud Monitoring.

Design and build event-driven systems with Apache Kafka — producers, consumers, Kafka Streams, CQRS, event sourcing, Schema Registry, and real-time data pipelines.

Master the four pillars of modern system integration: Request/Response REST, Event-Driven Architecture, Publish/Subscribe, and Messaging Systems. Learn when to use each pattern, how they work under the hood, and how to combine them into robust distributed systems.

Master enterprise integration from first principles to production governance. Covers SOA, APIs, messaging, event-driven architecture, integration patterns, security, testing, and operational maintenance — everything an Integration Architect needs to design and deliver robust integration solutions.

A university-level course on integrating information systems, applications, and data. Covers integration styles, APIs, design principles, enterprise patterns, messaging, security, monitoring, and data strategy — with practical exercises in schema mapping, data transformation, Pub/Sub with Node.js, and Robotic Process Automation.

Master PySpark for large-scale data engineering — Spark architecture, DataFrames, SQL, window functions, Delta Lake MERGE, Structured Streaming from Kafka, and production performance optimization.

Master pytest for data engineering — test discovery, fixtures, parametrize, mocking, pipeline testing with pandas DataFrames, CLI tools, FastAPI endpoints, and real database tests with Testcontainers.

Master Docker for data engineering — Dockerfile best practices, multi-stage builds for Python, docker-compose dev stacks (Airflow, Kafka, PostgreSQL), and production hardening with GitHub Actions CI/CD.

Master Google BigQuery — serverless Dremel architecture, partitioning and clustering, advanced SQL analytics (UNNEST, scripting, BigQuery ML), Python pipelines with the BQ client, cost optimization, and integration with Airflow and dbt.

Apply statistics to build smarter data pipelines — distributions, outlier detection, drift testing with KS and chi-squared tests, time series decomposition, CUSUM, and anomaly detection with Isolation Forest and Prophet.

Ace data engineering interviews — 50 SQL questions covering window functions, CTEs, SCD MERGE, and Snowflake/BigQuery specifics; 20 system design scenarios with ASCII diagrams; 30 Python pipeline questions on generators, mocking, and retry patterns.

Master every statistical concept that appears in ML and AI interviews — mean, variance, distributions, Bayes' theorem, probability, correlation, and sampling. Each chapter targets a specific interview topic with clear explanations, real AI examples, and common traps interviewers use.

The complete ML interview prep course — every concept from supervised learning and data splitting to regularization, hyperparameter tuning, and debugging production models. Structured as 14 chapters, each closing with a real interview scenario.

Everything interviewers ask about deep learning — neurons, backpropagation, CNNs, RNNs, transformers, activation functions, regularization, and real-world debugging scenarios. CS50-style: concept → intuition → interview answer.

Complete GenAI interview prep for 2026 roles — OpenAI & Azure OpenAI, Claude/Gemini, prompt engineering, model selection, RAG & smart search, AI agents, orchestration (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel), MCP, pharmacy/copilot use cases, and production guardrails.

Python from the perspective of an AI engineer — every concept, data structure, and standard library feature that appears in AI/ML interviews. 6 chapters covering core types, functions, OOP, built-in methods, NumPy patterns, and common interview problems.

Designing, explaining, and optimizing prompts for LLMs. Master zero-shot, few-shot, chain-of-thought, structured output, system prompts, and injection defense — every pattern asked in AI engineer interviews.

Top LLM interview questions with clear, structured answers. Covers how LLMs work, training pipelines, inference optimization, evaluation, and the most commonly asked conceptual questions for AI engineering roles.

Chains, prompts, memory, agents, and tools in LangChain. The most comprehensive LangChain interview preparation course — from basic LLMChain to production multi-agent systems with observability.

Embeddings, vector stores, and retrieval strategies for RAG. From basic similarity search to advanced hybrid retrieval, re-ranking, and production-grade RAG architectures.

Graph-based agent flows, state, and orchestration patterns using LangGraph. Learn how to build stateful, cyclical agent workflows that can loop, branch, and recover from errors.

Agent roles, task flows, and coordination in CrewAI. Learn how to structure multi-agent crews where each agent has a defined role, tools, and backstory — and how crews coordinate to complete complex tasks.

AutoGen fundamentals, agent coordination, and tool use. Learn Microsoft's AutoGen framework — how to create conversational agents that collaborate to solve problems through multi-turn dialogue.

Autonomous agents, planning, reflection, and tool use. The design patterns every AI engineer should know — from ReAct and Plan-and-Execute to self-reflection and multi-agent coordination.

Tool calling, function routing, and agent reasoning interview questions. Focused interview prep for AI engineer roles where you'll be asked to explain and implement agent tool-use patterns.

FastAPI questions and answers for serving AI/ML applications. Async patterns, Pydantic v2 validation, SSE streaming, dependency injection, background tasks, and production deployment — the backend stack for AI engineers.

Real-world AI engineering scenarios asked in interviews. How do you debug a hallucinating RAG system? How do you reduce LLM cost by 60%? These open-ended scenarios separate senior engineers from juniors.

Fine-tuning fundamentals, techniques, and trade-offs for interviews. From full fine-tuning to LoRA, QLoRA, and RLHF — every technique an AI engineer is expected to understand and explain.

Hybrid search, re-ranking, and chunking strategies asked in senior AI interviews. Go beyond basic similarity search to production-grade retrieval with query rewriting, cross-encoder re-ranking, and GraphRAG.