20 courses
Microsoft AI-103: Developing AI Apps and Agents on Azure
Prepare for the Microsoft AI-103 exam (Azure AI Apps and Agents Developer Associate), the successor to AI-102. Learn to plan, secure and manage Microsoft Foundry solutions, build generative apps and agents with the Foundry SDK, and implement vision, text, speech, retrieval and document extraction solutions with Azure AI Search and Content Understanding. Text-first lessons with runnable Python and REST examples, two hands-on labs, module quizzes, a capstone project and a long-form practice exam, aligned to the skills measured updated April 16, 2026.
Microsoft Azure AI Fundamentals (AI-901)
Prepare for the Microsoft AI-901 exam, the replacement for AI-900. Learn responsible AI, how generative models and agents work, and how to implement text, speech, vision and information extraction solutions with Microsoft Foundry and its SDK. Text-first lessons with short Python examples, check-yourself questions, module quizzes, a hands-on SDK lab and a long-form practice exam, aligned to the skills measured updated April 15, 2026.
AWS Certified Generative AI Developer – Professional (AIP-C01)
Prepare for the AWS Certified Generative AI Developer – Professional (AIP-C01) exam. Master all five exam domains: FM integration and RAG, agentic AI, AI safety and governance, operational efficiency, and testing and evaluation — with hands-on labs using Amazon Bedrock, Knowledge Bases, Agents, and Guardrails.
AWS Bedrock Developer: Production GenAI on AWS
Master Amazon Bedrock to build, secure, and deploy production-grade generative AI applications on AWS. From foundation model invocation and RAG pipelines to autonomous agents, guardrails, fine-tuning, and serverless deployment with Lambda and API Gateway.
AWS Machine Learning Engineer Associate (MLA-C01)
Master the full ML lifecycle on AWS to earn the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification. This course covers data preparation with SageMaker Data Wrangler and Feature Store, model training and tuning with SageMaker built-in algorithms and custom scripts, deployment strategies from real-time endpoints to batch transform, and end-to-end MLOps with SageMaker Pipelines, Model Monitor, and Model Registry.
Azure OpenAI Service & Responsible AI Deployment
Go from zero to production with Azure OpenAI Service — deploy GPT-4o, build retrieval-augmented generation (RAG) pipelines with Azure AI Search, implement content safety filters, and ship monitored, cost-efficient AI applications using Semantic Kernel. Built around Microsoft's Responsible AI principles and real enterprise deployment patterns.
MLOps Pipelines with Python and AWS
Build production-grade machine learning pipelines from experiment tracking to automated deployment. Master MLflow for experiment management, AWS SageMaker Pipelines for scalable training, FastAPI and Docker for model serving, and Evidently AI for drift monitoring — the full MLOps stack used at leading tech companies.
Mastering AI-Assisted Development: Cursor & Copilot
Master AI-assisted development with Cursor and GitHub Copilot. From inline completions and Composer to Agent Mode, context engineering, and advanced multi-file workflows — learn to 10x your productivity with the tools that are reshaping how software is built in 2026.
Deep Learning with PyTorch
Master deep learning from first principles using PyTorch 2.x. You will build and train neural networks from scratch, implement convolutional and recurrent architectures, apply attention mechanisms, and fine-tune pretrained models for real-world tasks. Every concept is grounded in working Python code and backed by the theory from leading university curricula.
AI Agents & Agentic Frameworks
A deep technical course on building production-grade AI agents. You will learn the theory behind agentic systems — the ReAct pattern, tool use, planning, and memory — then implement real agents using LangGraph, LangChain, and AutoGen. Topics include StateGraph construction with conditional routing and checkpointing, multi-agent orchestration with supervisor and handoff patterns, long-term memory with vector stores, and deploying observable, cost-controlled agents to production. Every concept is paired with runnable Python code drawn from the LangGraph, Anthropic, and AutoGen documentation.
Building RAG Applications
A hands-on, intermediate course on Retrieval-Augmented Generation (RAG). Move beyond basic prompting and learn to build production-grade RAG pipelines using LangChain and LlamaIndex — covering text chunking, embedding models, vector stores, hybrid retrieval, advanced patterns like HyDE and query decomposition, and evaluation with RAGAS. Every module includes real Python code you can run today.
Azure AI Engineer Associate (AI-102)
Master the Azure AI services and become a certified Azure AI Engineer Associate. This course covers every domain of the AI-102 exam: computer vision, natural language processing, Azure OpenAI Service, knowledge mining, and responsible AI governance. Hands-on labs use the Azure CLI and Python SDK throughout. **Important:** Microsoft is retiring the AI-102 exam on June 30, 2026 — enrol now to earn your certification before the deadline.
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