
Explore Practice Test: with Md Mehedi Hasan. This self-paced course is packed with value. No prior experience required - this course guides you from the basics to advanced Practice Test: concepts. This comprehensive course covers everything you need to know about Practice Test:.
Login to write a review and rate this course.
No reviews yet.
Click the 'Enroll Now' button. You will be redirected to the course page on Udemy (or another platform). The coupon code is usually automatically applied. If not, look for a code in the description or the 'Apply Coupon' section on the checkout page.
Yes, for most Udemy courses, once you enroll using the 100% off coupon, you get lifetime access and a certificate of completion upon finishing the course, just like a paid student.
Coupons have a limited number of redemptions or expire after a certain time (often 5 days or less). If a coupon is not working, it likely means it has expired or reached its maximum usage limit. We recommend checking our latest posts for fresh coupons.
Yes! Once you successfully enroll in the course while the coupon is active, you will have lifetime access to the course content, including future updates.
The courses are hosted on reputable platforms like Udemy. When you enroll with a 100% off coupon, the price should be $0.00, and you generally do NOT need to enter credit card information for free transactions on Udemy.

💡 Expert Insight: 💡 **Expert Insight:** This bootcamp uniquely cultivates the rapid prototyping and problem-solving agility crucial for modern AI roles. CompleponsCompleting 30 diverse projects in a month not only builds an undeniable, interview-ready portfolio but also instills the adaptability and iterative mindset demanded by AI's fast-evolving landscape.This AI Development Bootcamp is designed to guide learners through a series of 30 practical projects, each aiming to build foundational skills and a solid understanding of various AI concepts and machine learning techniques. The course begins with simple and approachable projects, gradually moving into more complex applications. By the end, participants will have an impressive portfolio of projects that span across diverse areas such as natural language processing, image classification, recommendation systems, predictive modeling, and more. Each project offers a hands-on learning experience and focuses on a particular machine learning concept, algorithm, or tool.The journey begins with creating a basic calculator using Python. This project introduces participants to coding logic and familiarizes them with Python syntax. Although simple, this project is essential as it lays the groundwork for understanding how to design basic applications in Python. From here, learners move to a more complex task with an image classifier using Keras and TensorFlow. This project involves working with neural networks, enabling learners to build a model that can distinguish between different classes of images. Participants will gain experience with training and validating a neural network, understanding key concepts such as activation functions, convolutional layers, and data preprocessing.A simple chatbot using predefined responses comes next, giving learners a taste of natural language processing. This project provides an introduction to building conversational agents, where the chatbot responds to user queries based on predefined rules. While it’s basic, it forms the foundation for more advanced NLP projects later on in the course. Moving on to the spam email detector using Scikit-learn, learners tackle text classification using machine learning. This project demonstrates how to process text data, extract relevant features, and classify messages as spam or not spam. Participants will work with techniques like TF-IDF vectorization and Naive Bayes, key tools in the NLP toolkit.Human activity recognition using a smartphone dataset and Random Forest introduces the concept of supervised learning with time-series data. Here, participants will use accelerometer and gyroscope data to classify various physical activities. This project showcases the versatility of machine learning in handling complex, real-world data. Following this, sentiment analysis using NLTK allows learners to dive deeper into NLP by determining the sentiment behind text data. This project involves cleaning and tokenizing text, as well as using pre-built sentiment lexicons to analyze emotional undertones in social media posts, reviews, or comments.Building a movie recommendation system using cosine similarity is another exciting project. Here, participants learn to create collaborative filtering systems, which are essential for personalizing user experiences in applications. By comparing user preferences and suggesting movies similar to what they have previously liked, participants gain insights into how recommendation engines function in popular platforms. Predicting house prices with linear regression then brings the focus back to supervised learning. Using historical data, learners build a model to predict house prices, introducing them to the basics of regression, data cleaning, and feature selection.Weather forecasting using historical data takes learners through time-series prediction, an essential skill for handling sequential data. Participants will explore different modeling approaches to forecast weather trends. Following this, the bootcamp covers building a basic neural network from scratch. Here, participants write their own implementation of a neural network, learning about the intricacies of forward and backward propagation, weight updates, and optimization techniques. This project offers a hands-on approach to understanding neural networks at a granular level.The course then progresses to stock price prediction using linear regression. This project teaches learners how to apply predictive modeling techniques to financial data, examining trends and patterns in stock prices. Predicting diabetes using logistic regression covers binary classification, where learners will predict the likelihood of diabetes in patients based on medical data. This project emphasizes the importance of healthcare data analytics and gives participants practical experience in building logistic regression models.The dog vs. cat classifier project with a CNN introduces convolutional neural networks. This is a key project in image classification, as participants work on creating a model that differentiates between images of cats and dogs. With this project, learners gain a practical understanding of how CNNs work for image recognition tasks. Next, the Tic-Tac-Toe AI using the Minimax Algorithm introduces the concept of game theory and decision-making. The AI will learn to play optimally, providing participants with a foundation in developing game AI.In credit card fraud detection using Scikit-learn, participants work on building a model that can identify fraudulent transactions, focusing on anomaly detection techniques. This project is highly applicable in financial services and demonstrates the importance of data-driven fraud detection systems. For Iris flower classification, learners utilize decision trees, one of the most interpretable machine learning algorithms. This project provides insight into how decision boundaries are formed and how simple classification algorithms operate.Building a simple personal assistant using Python speech libraries allows learners to integrate speech recognition and text-to-speech features. This project enhances programming skills in creating voice-activated applications. The text summarizer using NLTK helps participants explore text summarization techniques, which are useful in applications that require condensing information from large documents or articles. In fake product review detection, participants delve into NLP for identifying deceptive reviews, building skills that are crucial in maintaining integrity on e-commerce platforms.Detecting emotion in text using NLTK introduces emotion analysis, where participants will learn to classify text into categories such as happiness, sadness, anger, and more. This project is highly relevant for applications that require sentiment and emotion recognition. The book recommendation system using collaborative filtering is a practical extension of earlier recommendation techniques, allowing participants to explore more advanced methods for user personalization. Predicting car prices with Random Forest further reinforces regression and classification skills. Participants work on modeling car pricing, which is relevant in automotive industry applications.The course also includes identifying fake news using Naive Bayes, a critical skill in today’s information landscape. Participants will learn techniques to detect misinformation, equipping them with skills to work on data integrity projects. In the resume scanner using keyword extraction, learners create a tool for analyzing resumes and identifying relevant skills based on job descriptions. This project provides insights into how text matching can be used in HR applications. Finally, the customer churn prediction project teaches participants how to model customer behavior and predict churn, which is crucial for customer retention strategies in many industries.Throughout the course, each project builds on the concepts learned in previous projects, creating a comprehensive learning path. By working through these projects, participants will develop strong skills in data preprocessing, feature engineering, model training, evaluation, and deployment. They will also learn to work with different types of data, from text and images to time-series and tabular data. This bootcamp is structured to accommodate both beginners and those with some programming experience, providing a gradual learning curve that leads to increasingly complex applications.With each project, learners not only build technical skills but also improve problem-solving abilities. The course emphasizes real-world applications, helping participants see how AI techniques are used in industries such as finance, healthcare, e-commerce, entertainment, and more. The hands-on approach encourages creativity and experimentation, allowing learners to adapt and improve their models based on project requirements. By the end of the course, participants will have completed a diverse portfolio of projects that demonstrate their proficiency in AI and machine learning, giving them the confidence to tackle AI challenges independently.The bootcamp format is intensive but highly rewarding, designed to keep learners motivated and engaged. By dedicating a day to each project, participants immerse themselves in learning without overwhelming complexity, ensuring steady progress. The projects are structured to introduce core AI techniques incrementally, helping learners grasp each concept thoroughly before moving on to the next. This bootcamp is a unique opportunity to acquire industry-relevant skills in a short period, making it ideal for anyone interested in breaking into the field of AI or enhancing their technical abilities.Who this course is for:Aspiring AI Professionals: Individuals who want to break into the field of artificial intelligence and machine learning. Whether you are a beginner or someone with a technical background looking to transition into AI, this course will provide a comprehensive and practical foundation.Data Scientists and Analysts: Professionals working with data who want to upskill and incorporate AI and machine learning techniques into their data analysis pipelines. This course will help data professionals apply AI techniques to enhance data-driven decision-making.Software Engineers and Developers: Developers with experience in software engineering who are looking to dive into AI-driven development, build machine learning models, and integrate AI solutions into their applications.Students and Researchers: University students, Ph.D. candidates, or academic researchers interested in AI for academic projects or advancing their careers in academia or industry research roles. This course will give them hands-on experience with AI tools and frameworks.Tech Enthusiasts and Self-learners: Enthusiasts and hobbyists who are passionate about AI and want to gain a structured and practical approach to understanding AI concepts, machine learning algorithms, and how to apply them in real-world scenarios.

Welcome to Agentic AI for Product Owners, the ultimate non-technical course designed to help product managers, product owners, and business leaders understand and communicate the power of Agentic AI. As AI agents become the cornerstone of next-generation digital solutions, product professionals are expected to speak the language of autonomy, orchestration, and intelligent automation—even without a coding background.This course is built specifically for non-technical professionals who want to confidently lead AI projects, collaborate with AI teams, and drive business value using AI agent solutions.You’ll learn what makes Agentic AI different from chatbots, how it works behind the scenes (in plain English), and how to identify real-world use cases that align with your product goals. You’ll also build the skills to scope an AI agent MVP, communicate your vision to stakeholders, and evaluate ethical and operational considerations without getting lost in jargon.Whether you're in SaaS, healthcare, finance, operations, customer support, or marketing, this course will empower you to become the go-to AI voice on your product team.What You’ll Learn:The foundations of Agentic AI—how it differs from traditional AI or simple automationKey concepts like autonomy, memory, tools, and orchestration without touching codeHow to identify the right AI use cases in your company using real industry examplesHow to scope, prioritize, and plan agentic projects with clear MVP thinkingTechniques to communicate AI solutions to stakeholders in clear, outcome-driven languageA step-by-step guide to crafting and pitching your own AI agent product visionHow to navigate AI ethics, privacy risks, and human-in-the-loop safety mechanismsThe ability to simulate stakeholder meetings, respond to questions, and build confidence as a product leaderWho This Course Is For:Product Owners and Product Managers seeking to work in AI-enhanced environmentsNon-technical stakeholders in tech companies who want to understand AI agentsTeam leads, strategy heads, and operations managers responsible for evaluating or adopting AI toolsProfessionals looking to build AI literacy without becoming machine learning engineersWhy Take This Course Now?The rise of LLMs, intelligent agents, and AI-powered automation is transforming how products are built, launched, and scaled.Companies like Notion, Salesforce, HubSpot, and Zendesk are deploying AI agents in customer service, sales, HR, and finance—reshaping the expectations for product teams.Don’t fall behind. This course gives you a competitive edge, allowing you to bring AI strategy and communication into your product leadership toolkit—even if you’ve never written a single line of code.Tags / Keywords:Agentic AI, AI for product managers, AI agent solutions, non-technical AI course, product owner AI training, AI strategy for PMs, intelligent agents, AI product communication, AI use case identification, AI ethics, autonomous AI, AI product pitch, LLM orchestration, AI product MVP, no-code AIWho this course is for:Product leaders working on or exploring AI-enabled featuresTeam leads and decision-makers who need to evaluate or explain AI agent solutionsOperations, marketing, support, or finance professionals collaborating with AI and data teamsFounders, consultants, and strategists looking to stay ahead in an AI-driven marketAnyone who wants to understand Agentic AI in simple, strategic terms and lead with confidence in stakeholder discussions

💡 Expert Insight: 🚀 **Career Boost:** This course uniquely positions you at the critical intersection of cutting-edge AI, dominant cloud infrastructure (AWS), and industry-recognized certification. It transforms theoretical AI knowledge into deployable, real-world solutions, making you an indispensable asset in a market desperate for skilled practitioners who can build and manage AI on a production-ready platform.This comprehensive course, "Mastering AI on AWS: Training AWS Certified AI Practitioner" is designed to equip you with the knowledge and skills to excel in AI and machine learning using AWS services. Whether you're a cloud professional, developer, or AI enthusiast, this course will guide you through the fundamentals of AI and machine learning while providing hands-on experience with cutting-edge AWS AI services like Amazon SageMaker, Rekognition, Comprehend, Polly, and more.Starting with foundational concepts of AI and machine learning, you’ll progress through practical labs, working with real-world applications such as image and video recognition, natural language processing, and recommendation systems. The course will also cover security best practices, responsible AI, and preparing for the AWS Certified AI Practitioner exam. By the end, you’ll be ready to build, deploy, and monitor AI applications on AWS and confidently pass the certification exam.Through engaging lessons, hands-on projects, and practical exercises, this course ensures you develop both theoretical knowledge and practical skills to succeed in the growing field of AI and machine learning.What you'll learn:Fundamental concepts of AI, machine learning, and AWS AI services.How to build and deploy AI applications using Amazon SageMaker, Rekognition, Comprehend, Polly, and more.Best practices for securing AI and machine learning workflows on AWS.How to prepare for and pass the AWS Certified AI Practitioner exam. Who this course is for:Cloud professionals wanting to expand into AI/ML.AI/ML enthusiasts looking to gain practical skills using AWS services.Aspiring data scientists and developers seeking to implement real-world AI solutions.Students and professionals preparing for the AWS Certified AI Practitioner exam.Who this course is for:Cloud Professionals Seeking to Expand into AI/ML: Cloud engineers, architects, and developers who are familiar with AWS and want to build a foundation in AI and machine learning technologies using AWS services.AI and Machine Learning Enthusiasts: Individuals interested in learning the fundamentals of AI and machine learning, and how to apply these technologies in real-world scenarios using AWS.Aspiring Data Scientists and ML Engineers: Beginner data scientists and machine learning engineers looking to gain hands-on experience with AWS AI services, such as Amazon SageMaker, and learn how to build, deploy, and manage AI models on the cloud.Business Analysts and Decision Makers: Professionals who want to understand the capabilities of AI and ML on AWS in order to make informed decisions, manage AI projects, and leverage AI technologies to solve business problems.Students Preparing for AWS AI Certification: Anyone preparing for the AWS Certified AI Practitioner exam who needs structured learning materials, practice labs, and exam preparation resources to ensure success.Tech Professionals Looking to Upskill: IT professionals, developers, and cloud practitioners who want to enhance their career prospects by gaining AI and machine learning skills in a cloud environment.

Are you ready to build intelligent AI agents that go beyond simple prompts and one-off answers? In today’s fast-evolving AI landscape, it’s not just about Large Language Models (LLMs)—it’s about giving them the right context to think, reason, and act. This course will teach you how to master the art and science of context design so your agents can perform complex tasks, sustain multi-turn conversations, and integrate with real-world tools and memory systems.In Mastering Context Design for Intelligent AI Agents, you’ll learn how to design agents that are context-aware, adaptive, and highly capable. You’ll discover how to work with six foundational context types: instructional context, example-based context, knowledge context, memory context, tool context, and tool result chaining. These aren’t just theory—they’re the building blocks behind real-world agent frameworks like LangChain, CrewAI, LangGraph, and OpenAI’s function calling systems.We’ll show you how to move beyond static prompting into modular, orchestrated systems that automatically manage and update context over time. Whether you’re building a Document Q&A bot, a multi-agent workflow, or a self-reflective planner agent, this course will guide you step by step.You'll learn how to:Use prompt engineering effectively with role, goal, and requirement structuresImplement few-shot prompting using positive and negative examplesLeverage semantic search and vector databases for dynamic retrievalArchitect short-term and long-term memory using modern toolsIntegrate tools through function calling, with clear parameter design and output handlingOptimize token usage with prompt compression and memory pruningCreate self-improving agents through reflection and autonomous context refreshBuild multi-context pipelines using agent orchestration frameworksThis course is perfect for developers, AI engineers, technical product managers, and prompt engineers who want to move beyond beginner prompt patterns and develop real-world, production-grade AI agents.By the end of the course, you’ll be able to:- Design context-rich prompts for advanced use cases- Build modular agent workflows with dynamic context injection- Implement agents using LangChain, CrewAI, or OpenAI Assistants API- Apply token-efficient strategies to keep costs low and performance high- Debug, reflect, and improve agent behavior in autonomous systemsNo prior deep learning experience is required—just a working knowledge of prompts, tools, and a curiosity for how autonomous agents really work under the hood.If you're aiming to lead the way in AI automation, agentic systems, or LLM-powered workflows, this course is your blueprint.Who this course is for:A Prompt Engineer who wants to move beyond templates into modular, agentic designA Software Developer or AI Engineer building multi-step LLM-based applicationsA Technical Product Manager designing features powered by agents or assistantsA Data Scientist experimenting with autonomous decision-making systemsAn AI Enthusiast curious about how tools like LangChain, OpenAI Assistants, and CrewAI really workA Researcher or Educator looking for deeper insight into contextual design principles for agents

Welcome to Vibe Coding and Software 3.0, a cutting-edge course that will redefine how you build, scale, and maintain intelligent systems in the age of AI. This isn’t just another programming tutorial—it’s a deep dive into the future of software engineering, where AI-assisted development, specification-driven architecture, and microservices come together to form the foundation of Software 3.0.In a world where AI tools like ChatGPT, Copilot, and Claude are reshaping how developers write code, manage APIs, and design infrastructure, it’s no longer enough to just “know how to code.” Today’s top engineers must understand how to collaborate with AI, generate code from prompts, build autonomous systems, and orchestrate microservice architectures that are scalable, secure, and observable.This course introduces you to the Vibe Coding paradigm—a developer workflow that begins with human intent, uses natural language specifications, and leverages AI to scaffold everything from API contracts to documentation. Whether you’re designing a RESTful service, building a GraphQL backend, or deploying to Kubernetes, Vibe Coding enables you to move faster while maintaining clarity, control, and code quality.You’ll learn how to:Use AI prompts to generate and test APIs using OpenAPI and GraphQL schemasBuild secure, scalable microservices with AI-guided code generationImplement best practices in authentication, including API Keys, OAuth2, and JWTsLeverage AI to auto-document your services with Swagger and context-aware chatbotsApply Specification-Driven Development (SDD) in embedded, edge, and cloud systemsMonitor performance, detect anomalies, and visualize observability data with OpenTelemetryUse distributed tracing, root cause analysis, and explainable dashboards powered by AIBuild your own prompt libraries, knowledge graphs, and developer chatbots for ongoing productivityDeploy production-grade systems across diverse domains like e-commerce, IoT, healthcare, and gamingBy the end of the course, you’ll complete a Capstone Project where you select a real-world use case, define its spec, prompt AI to generate code, build a full-stack system, and present it with documentation and an architecture map. You’ll not only gain technical skills, but also the ability to communicate your systems like a modern software architect.This course is perfect for software engineers, DevOps professionals, tech leads, and product-minded developers who want to stay ahead of the curve. Whether you're transitioning into AI-powered development or leveling up your microservice architecture game, this course will equip you with the mindset, tools, and hands-on skills to thrive in the AI-first software era.Are you ready to code with intent, not syntax? To lead in the age of intelligent systems and developer-AI collaboration?Then welcome to Vibe Coding and Software 3.0—where your specs become software, and your ideas scale with AI.Who this course is for:Product-minded engineers who want to combine speed with scalabilityDevelopers exploring tools like OpenAPI, GraphQL, Kubernetes, and OpenTelemetryProfessionals aiming to build secure, scalable systems powered by AI-generated codeEarly-career developers eager to learn the mindset and tooling behind Software 3.0

💡 Expert Insight: 🚀 **Career Boost:** In an era where basic GenAI prompts are commoditized, this course positions you as an indispensable architect capable of solving enterprise-level challenges. By mastering the integration of Knowledge Graphs, RAG, and multi-agent systems, you'll unlock the ability to design GenAI solutions that are not just intelligent, but also factual, auditable, and truly scalable for complex business applications.The Certified Generative AI Architect with Knowledge Graphs program is a comprehensive, advanced-level certification designed to equip professionals with the tools, frameworks, and hands-on experience to architect cutting-edge Generative AI (GenAI) systems that are intelligent, explainable, and scalable. This course combines the power of Large Language Models (LLMs), Knowledge Graphs, Retrieval-Augmented Generation (RAG), and agent-based orchestration to help you build production-grade AI solutions that go beyond simple prompt engineering.You’ll begin by mastering the foundations of GenAI architecture, including the capabilities of modern LLMs, the rise of agentic AI systems, and how memory, context, and reasoning enhance performance. You'll explore the anatomy of RAG pipelines, dive into context-aware generation, and set the stage for knowledge-enhanced AI applications.From there, we’ll delve deep into semantic technologies. You’ll learn to design and build ontologies using tools like Protégé and TopBraid Composer, construct and query graph databases using RDF, OWL, SPARQL, and Cypher, and manage the lifecycle of enterprise-grade knowledge systems. This enables you to drive semantic search, entity disambiguation, and structured reasoning in real-world deployments.The course also covers how to build hybrid retrieval systems by integrating vector databases like FAISS, Weaviate, and Pinecone with graph-based reasoning. You'll implement advanced RAG pipelines that combine semantic filtering, graph traversal, and vector similarity to improve contextual relevance and reduce hallucinations in LLM outputs.In the advanced sections, you’ll orchestrate multi-agent GenAI systems using frameworks like LangGraph, CrewAI, and AutoGen. These agents will collaborate across planning, retrieval, reasoning, and summarization tasks—enabling intelligent workflows that are modular, traceable, and extensible.You’ll also learn cloud-native deployment strategies across AWS, Azure, and GCP, mastering containerization, Kubernetes, and serverless architectures for scalable GenAI APIs. Monitoring, observability, and secure rollout patterns are all covered, ensuring your solutions are enterprise-ready.To bring it all together, you’ll engage in a capstone project where you define a business problem, create an ontology, build a knowledge graph-enabled RAG pipeline, deploy a multi-agent application to the cloud, and present your solution with full documentation and executive-ready architecture blueprints.This course is ideal for AI architects, ML engineers, semantic web practitioners, and cloud-native developers seeking to lead the next wave of intelligent, knowledge-aware AI systems.Whether you’re building AI for healthcare, legal tech, finance, or retail—this certification will help you deliver impactful, explainable, and future-proof GenAI systems powered by knowledge graphs.Who this course is for:AI/ML Engineers looking to deepen their understanding of LLMs, RAG pipelines, and knowledge-aware AI applications.Solution and Cloud Architects who want to design scalable, secure, and context-aware GenAI systems using modern deployment patterns and cloud-native tooling.Data Engineers and Knowledge Graph Practitioners who are expanding into Generative AI and want to leverage RDF, OWL, SPARQL, and graph models in AI workflows.Technical Product Managers and Tech Leads who need to understand how to structure multi-agent systems, integrate LLMs with enterprise data, and align technical architectures with business goals.Semantic Web or Ontology Engineers aiming to apply their expertise in the fast-evolving world of LLMs, agentic workflows, and context-driven GenAI applications.
Get access to 115+ premium paid courses collection with 8000+ videos and 3500+ files on Telegram
Get access to 115+ premium paid courses collection with 8000+ videos and 3500+ files on Telegram