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Generative AI for Absolute Beginners [2026]

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0(320 students)

Self-paced

All Levels

About this course

Are you curious about Generative AI but don’t know where to start?This course is designed specifically for absolute beginners who want to understand and actually use AI in real life—without any technical background or coding experience.You’ll start with the fundamentals—what Generative AI is, how it differs from traditional AI, and where it’s being used today. Then, you’ll quickly move into hands-on learning, where you’ll use tools like ChatGPT to create content, generate ideas, and solve real-world problems.One of the most powerful skills you’ll learn is Prompt Engineering—how to ask AI the right way. Using a simple and practical framework, you’ll learn how to get better, faster, and more accurate results from AI.We’ll also break down complex concepts like LLMs, tokens, embeddings, transformers, and multimodal AI in a way that’s easy to understand—even if you’re completely new to AI.As you progress, you’ll explore advanced topics such as RAG (Retrieval-Augmented Generation), AI Agents, and Agentic AI, and understand how modern AI systems actually work behind the scenes.By the end of the course, you’ll build your own AI-powered chatbot project, giving you real, practical experience you can apply immediately.You’ll also learn about limitations, common mistakes, misuse, and Responsible AI, so you can use AI effectively and ethically.If you’ve ever felt that AI is powerful but confusing—this course will give you the clarity and confidence to start using it today.

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Duration: Self-paced

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AI systems introduce security challenges that are fundamentally different from anything traditional cybersecurity was built to handle. LLM applications, retrieval pipelines, vector databases, and agent based automations create new vulnerabilities that can expose sensitive data, enable unauthorized actions, and compromise entire workflows. This course gives you a complete and practical framework for securing GenAI systems in real engineering environments.You will learn how modern AI threats operate, how attackers exploit prompts, tools, and connectors, and how data can leak through embeddings, retrieval layers, or model outputs. The course walks you through every layer of the AI stack and shows you how to apply the right defenses at the right places, using a structured and repeatable security approach.What you will learnThe full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layersHow GenAI attacks work, including injection, leakage, misuse, and unsafe tool executionHow to use AI firewalls, filtering engines, and policy controls for runtime protectionAI SDLC best practices for dataset security, evaluations, red teaming, and version managementData governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddingsIdentity and access patterns that protect AI endpoints and tool integrationsAI Security Posture Management for risk scoring, drift detection, and policy enforcementObservability and evaluation workflows that track model behavior and reliabilityWhat is includedArchitecture diagrams and control mapsModel and RAG threat modeling worksheetsGovernance templates and security policiesChecklists for AI SDLC, RAG security, and data protectionEvaluation and firewall comparison frameworksA complete AI security control stackA step by step 30, 60, 90 day rollout plan for teamsWhy this course is essentialIt focuses on practical security for real AI deploymentsIt covers every critical layer of modern LLM and RAG systemsIt delivers ready to use tools and artifacts, not theoryIt prepares you for one of the fastest growing and most demanded areas in techIf you need a structured and actionable guide to protecting AI systems from modern threats, this course provides everything required to secure, govern, and operate GenAI at scale with confidence.

AI Security Fundamentals: Risks, Frameworks & Tools

Udemy Instructor

Modern AI applications introduce security challenges that traditional defenses cannot address. LLM based systems, retrieval pipelines, agents, data connectors, and vector databases expose new attack paths that organizations must understand and control. This course gives you a complete, practical, and engineering focused approach to securing GenAI systems across their entire lifecycle.You will learn how attackers exploit AI models, how sensitive data leaks through prompts and outputs, how RAG pipelines can be manipulated, and how misconfigured tools or connectors expose entire environments. The course shows you how to design secure AI architectures, apply the right controls at the right layers, and build a repeatable security process for any AI powered system.What this course includesA detailed AI Security Reference Architecture for models, prompts, data, tools, and monitoringFull coverage of GenAI threats: injection attacks, data leakage, model misuse, unsafe toolsPractical guardrail design using AI firewalls, filtering, and permissioningAI SDLC guidance for dataset integrity, evaluations, red teaming, and version controlData governance for RAG systems: access control, filtering logic, encryption, secure embeddingsIdentity and authorization models for AI endpoints and tool integrationsAI Security Posture Management workflows for monitoring risk and driftObservability pipelines for logging prompts, responses, decisions, and quality metricsWhat you getArchitecture blueprintsThreat modeling templatesGovernance and policy frameworksSecurity checklists for AI SDLC and RAGEvaluation and firewall comparison matricesA full AI security control stackA clear 30, 60, 90 day adoption roadmapWhy this course is valuableIt is built for real engineering and real enterprise environmentsIt covers the full AI ecosystem instead of focusing on a single controlIt provides the exact artifacts professionals need to secure AI systemsIt prepares you for one of the most in demand skill sets in modern techIf you need a practical, structured, and comprehensive guide to securing LLM and RAG applications, this course gives you the tools, knowledge, and processes required to protect AI systems with confidence and to operate them safely at scale.

Software architecture

Udemy Instructor

The architecture describes the processes and structures of the entire project, determining the data throughput in different parts of the project, and allows you to optimize and scale the project in the future.Good architects are often people who learn from even better architects. Perhaps this is because some conceptual requirements are inherent in almost all projects. Each conceptual requirement is formulated as a question that the architect must ask himself during the project. Of course, some systems may have their own additional critical conceptual requirements.We will get to know the types of software architectures. We will be able to create our own simple software architecture. We will be able to use and expand the software architecture. As a result, this course will be useful not only for architects or people who plan to become one. And, technical leaders of the development group, developers of different levels and testers.In first section, I reveal the main objective of the course. In second section you will be introduced to the types of software architectures. In third section you will be able to create his own simple software architecture. In forth section you will be able to use and extend the software architecture. In fifth section the nuances of software architecture will be considered. Six section will help you understand which part of the course will be most useful to you.Course Topics and Structure:1. Introduction2. Familiarity with software architecture.2.1 Introduction2.2 Principles of software architecture.2.3 The content of the architecture.2.4 Static structural diagrams.2.5 Dynamic diagrams.2.6 Conclusions.3. Creation of software architecture.3.1 Introduction3.2 How to start creating software architecture.3.3 The interface and what it should describe.3.4 Interaction of components and objects.3.5 Database. Content, objects, interaction.3.6 Dynamic diagrams, how to do and why you need them.3.7 Conclusions.4. Support and use of software architecture.4.1 Introduction4.2 What to do with the software architecture after its creation.4.3 Helpful information for the programmer on software architecture.4.4 Support of software architecture.4.5 Extending the software architecture.4.6 Conclusions.5. Software architecture. Next level.5.1 Introduction5.2 Summary of the main structures.5.3 Impact of software architecture on the future life of software.5.4 A software architect is not a profession, but a calling.5.5 Twelve architecture mistakes.5.6 Conclusions.6. Conclusions and results of the course. Summing up.

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Generative AI for Absolute Beginners [2026]

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