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we can make conclusion like: the two sentences are related according to cosine similarity result

You can do like this

Two sentences are given—“The men are playing soccer on the beach” and “The boys are playing soccer near the beach.” Write Python program(s) to determine cosine similarity and Jaccard similarity. Do these distance metrics agree?

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🎙Workshop: Enterprise System 📅 Date: November 5, 2025 ⏰ Time: 8:00 PM 🎤 Host: Abate Alemu 📍 Live on our Telegram Channel: In this session We'll ✅ Understanding the fundamentals of Enterprise Systems ✅ How Enterprise Systems integrate business processes ✅ Challenges in implementing Enterprise Systems ✅ Career paths and opportunities in Enterprise System development 👤 Guest Speaker: Dr. Workshet Debay ✨ Instructor at School of Information Science AAU. 📢 Stay connected: Telegram|LinkedIn|YouTube | Tiktok |

💻 How to Learn Frontend Development in 2025 – Step by Step 🌐✨ ✅ Tip 1: Start with HTML & CSS ⦁ Learn HTML structure: semantic tags, forms, tables, links, plus ARIA for accessibility and SEO basics. ⦁ CSS basics: selectors, box model, flexbox, grid, animations, variables, and responsive design with media queries/mobile-first. In 2025, focus on semantic HTML5 for better AI crawling too! ✅ Tip 2: Master JavaScript ⦁ Variables, data types, loops, functions, plus DOM manipulation and event handling. ⦁ ES6+ features: arrow functions, template literals, destructuring, promises, async/await, and modules. Practice with modern JS to handle async data flows smoothly. ✅ Tip 3: Learn Version Control ⦁ Git basics: commit, push, pull, branching, and merging. ⦁ Use GitHub for hosting projects, collaboration, and even GitHub Actions for CI/CD starters. Essential for team workflows in today's remote dev world. ✅ Tip 4: Explore Frontend Frameworks ⦁ React.js: components, props, state, hooks, plus Next.js for SSR and AI integrations like server components. ⦁ Alternatives: Vue.js 3+ for lightweight apps, Angular for enterprise-scale. ⦁ Component-based architecture—React dominates 2025 job listings. ✅ Tip 5: Work with APIs ⦁ Fetch data using fetch or axios, handle JSON and REST/GraphQL APIs. ⦁ Display API data dynamically, with error handling and loading states. Tie this to real APIs like weather or news for dynamic UIs. ✅ Tip 6: Learn CSS Frameworks & UI Libraries ⦁ Tailwind CSS or Bootstrap for rapid, utility-first styling. ⦁ Material UI or Chakra UI for React—add Headless UI for accessible components. These speed up prototyping without sacrificing custom looks. ✅ Tip 7: Optimize for Performance ⦁ Minify CSS/JS, lazy load images/components, and use code splitting. ⦁ Core Web Vitals: Monitor LCP, FID, CLS with tools like Lighthouse. In 2025, PWA features and edge caching are key for fast, app-like experiences. ✅ Tip 8: Build Projects ⦁ Portfolio website to showcase your work. ⦁ Todo app, weather app, or e-commerce frontend with API pulls. ⦁ Add a blog or chat app—deploy to Vercel/Netlify for live demos. ✅ Tip 9: Testing & Debugging ⦁ Browser DevTools: inspect, console, network tab for troubleshooting. ⦁ Unit testing with Jest or React Testing Library, plus end-to-end with Cypress. Catch bugs early to build reliable, production-ready code. ✅ Tip 10: Keep Learning & Stay Updated ⦁ Follow blogs like Dev.to, Smashing Magazine, newsletters, and YouTube (Fireship for quick tips). ⦁ Join communities: Reddit's r/Frontend, Discord groups, Stack Overflow. Track trends like WebAssembly or AI-assisted coding tools. 💬 Tap ❤️ if this helped you! @Ethio_Coders_channel

The Machine Learning Way You show the computer thousands of pictures of cats and also thousands of pictures that are not cats. The computer looks for patterns in all these pictures by itself. Slowly, it learns to build its own "idea" of what makes a cat a cat. It figures out the rules on its own. After this training, when you show it a new picture it has never seen, it can tell you, "That's a cat!" with a high degree of confidence.

It's Day Two: concept of Machine Learning

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AI vs ML Artificial Intelligence And Machine Learning

AI vs. ML: What's the Difference? Think of it as a hierarchy: Artificial Intelligence (AI) is the broad goal of creating machines that can perform tasks that typically require human intelligence. This includes everything from logic and problem-solving to understanding language. Machine Learning (ML) is the primary method we use to achieve AI. Instead of being explicitly programmed for every rule, an ML model learns patterns from large amounts of data. The Simple Analogy: AI is the concept of a "self-driving car." ML is the specific technology that allows the car to recognize a stop sign by training on thousands of images. In short: All ML is AI, but not all AI is ML.

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The foundational concept of Artificial Intelligence (AI) centers on creating an Intelligent Agent, which is a system designed to perceive its environment and execute a sequence of actions that maximizes its chances of achieving a specific goal, moving beyond simple programmed routines. For instance, a delivery robot operates as such an agent, utilizing Perception through sensors like cameras and LIDAR to understand its current state (location, obstacles). The robot then employs a Search or Planning Algorithm, a key component of classical AI, to model the world and find the most cost-effective path from its starting point to the target shelf. This process is governed by a Rule-Based System containing explicit, human-coded logic (e.g., "IF obstacle is close THEN stop and initiate detour") that dictates its immediate behaviors, allowing the robot to exhibit rational, goal-directed intelligence by searching for optimal actions within its defined constraints and knowledge base.