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AI, ML and Generative AI - for Managers and Beginners

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5(332 students)

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Learn Artificial Intelligence, Machine Learning and Generative AI in the simplest possible way. Make yourself AI aware. End of this course, you will be in a position to speak AI with othersDesigned for beginners with ZERO knowledge on AI.

No Coding skill requiredThis course is designed to cover everything about AI in a high level. It covers all the key concept of AI in a beginners friendly manner with easy to understand examples. You will Learn:Fundamental of Artificial Intelligence, Machine Learning and Generative AI Concept of machine learning and its relation to AISupervised Learning, Unsupervised Learning & Reinforcement Learning in Machine LearningTypes of Data - Labelled Data & Unlabelled DataTypes of Data - Structured Data & Unstructured DataStages in Machine LearningOverfitting & UnderfittingWhat is Natural Language Processing?

How Artificial Neural Network works? Foundation Models, Large Language Models, Diffusion ModelsBasic of Deep LearningMulti-modal ModelsToken and Embedding in AIContext Window of a modelKnowledge Cutoff of a modelHallucinationGrounding and RAG in AIAll you need to know about Prompt EngineeringWho Should Take This Course? This course is designed for anyone interested in learning about AI, including:Beginners who wants to understand AI without coding.

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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.

Software architecture

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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.

Improving software development productivity.

Udemy Instructor

What will be in the course:Personal experience;How I structured the work in my team;What my team and I use;What are the alternatives to my tools;Which helped speed up development.What this course will not include:A huge amount of theory;Calculations that prove how to design;Imposing opinions and proving that problems need to be solved only this way and nothing else.As a result, this course will be useful not only for the technical leader of the development group, but also for managers of different levels, developers and testers.The topics of the lessons that will be discussed in this course are shown on the slide:Introductory lesson;Team and roles in it;Technical task;Work planning;Task management systems;Architecture;Version control systems, version accounting;Codestyle;Description of the code;Automatic generation of documentation;Testing;Bug life cycle, Bug tracker;Automatic tests;Automatic machines and scripts;Instructions;Summing up and my results.For each topic, there will be an explanation of how this innovation in the development process will help increase efficiency.By efficiency, I mean the following measured parameters:The number of mechanical errors will decrease;Development time will be reduced;The error search time will decrease;The time to attract a new developer will decrease;The accuracy of the development forecast and adherence to plans is increased.And not measurable, which will also get better:It will become easier to hand over the product to the customer;Developers' interest will increase.

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AI, ML and Generative AI - for Managers and Beginners

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