🚀 Day 21 of My Generative & Agentic AI Journey! Today’s focus was on understanding how to import functions and modules in Python — an important step towards organizing code in real-world projects. Here’s what I learned: 📦 Importing Modules: • We can import an entire module and access its functions using dot notation 👉 Example: import math Using functions like math.sqrt(), math.floor() 📥 Importing Specific Functions: • Instead of importing everything, we can import only required functions 👉 Example: from math import sqrt, ceil 👉 Makes code cleaner and avoids unnecessary imports ⚠️ import * (Not Recommended): • Using import * brings all functions and variables into the current namespace • Can cause confusion and naming conflicts 👉 Better to explicitly import only what is needed 💡 Key takeaway: Proper use of imports helps in writing modular, clean, and maintainable code — especially in large projects. Taking one more step towards writing structured and scalable applications 🚀 #Day21 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🚀 Excited to share my latest project: Student Performance Prediction System I built a Machine Learning web application that predicts student performance based on various academic and demographic factors. 🔍 Key Highlights: • End-to-end ML pipeline (data preprocessing → training → prediction) • Built using Flask for deployment • Clean and interactive UI • Model serialization using dill 🌐 Live Demo: https://lnkd.in/gGBekFvt 💻 Tech Stack: Python, Scikit-learn, Pandas, NumPy, Flask This project helped me strengthen my understanding of real-world ML deployment and pipeline design. I would love your feedback and suggestions! 🙌 #MachineLearning #DataScience #Python #Flask #AI #StudentProject #MLProjects

🚀 Built an AI-Powered Exam Assistant using RAG (Retrieval-Augmented Generation) Preparing from long PDFs can be overwhelming and time-consuming. I built this project to make exam preparation smarter, faster, and more interactive. 💡 What it does: 📄 Upload PDFs (notes, books, lectures) ❓ Ask questions directly from your content 🧠 Get accurate, context-aware answers using RAG 📝 Generate MCQs, summaries, and short/long answers instantly ⚙️ Tech Stack: FastAPI | Streamlit | Python | RAG Architecture 🔍 This project demonstrates how Retrieval-Augmented Generation can be applied to build practical AI tools for education. 🎯 The goal was simple: turn static study material into an interactive learning assistant. 🔗 GitHub Repository: 👉 https://lnkd.in/dmfiqnbs #AI #MachineLearning #Python #FastAPI #Streamlit #RAG #GenAI #EdTech #ArtificialIntelligence #Projects #Students

🚀 Day 18 of My Generative & Agentic AI Journey! Today’s focus was on understanding the return statement in Python functions and how it controls the output of a function. Here’s what I learned: 🔙 Return in Functions: • return is used to send a value back from a function 👉 We can return strings, numbers, or any data type • If we use print instead of return 👉 The function outputs None when we try to store its result • If nothing is returned explicitly 👉 Python automatically returns None 🔢 Types of Returns: • Single value → Function returns one value • Multiple values → Function can return multiple values together • Early return → Function can exit before completing all steps 👉 Useful when a condition is met early 💡 Key takeaway: return makes functions more useful and reusable by allowing them to send results back instead of just displaying output. Understanding this helps in writing cleaner and more functional code 🚀 #Day18 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

📊 Project Showcase: Student Performance Predictor Developed a machine learning model to predict student academic performance using features like study time, absences, and parental support. 🔧 Implementation: • KNN Algorithm • Data preprocessing & scaling • Model deployment using Flask • Frontend integration with React This project demonstrates end-to-end ML workflow from data to deployment. 🔗 GitHub Repository: https://lnkd.in/dkwmXV-n #DataScience #MachineLearning #AI #Python #ProjectShowcase

94 pages. Every URL, title & description. Mapped in 3.6 seconds flat. Here's how I used Firecrawl to build a course search tool for students , without touching a single line of HTML. What I got back instantly: → Every course page → Faculty-wise pages with batch details → Descriptions ready for semantic search or RAG I fed this directly into an LLM-powered search index. Now students can ask: "Show me all batches subject-wise for May 2026" ...and get accurate results, without touching a single HTML tag. This is what I mean when I say Firecrawl is infrastructure for AI, not just a scraper. The [/map](https://lnkd.in/dsJ7c4nH)  endpoint is criminally underrated. One call. Structured output. Production-ready. What website would you map first? Drop it below — I'll show you what the output looks like. 👇 #Firecrawl #WebScraping #RAG #AItools #Python #BuildInPublic #EdTech

🚀 Day 19 of My Generative & Agentic AI Journey! Today’s focus was on exploring different types of functions in Python and how they are used in real-world programming. Here’s what I learned: ⚙️ Pure vs Impure Functions: • Pure Functions → Always return the same output for the same input and don’t modify external data 👉 More predictable and easier to test • Impure Functions → Depend on or modify external variables 👉 Less predictable, generally avoided in clean code 🔁 Recursive Functions: • A function that calls itself to solve a problem step by step 👉 Example use case: Breaking a problem into smaller parts (like factorial, countdown, etc.) ⚡ Lambda (Anonymous) Functions: • Small, one-line functions without a name • Useful for short operations where defining a full function is unnecessary 👉 Example use case: Quick calculations or transformations 💡 Key takeaway: Understanding different types of functions helps in writing cleaner, efficient, and more maintainable code. Slowly moving towards writing optimized and professional-level Python 🚀 #Day19 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

Just finished Anthropic’s Introduction to Model Context Protocol — definitely worth the time. Learned how MCP lets AI models like Claude connect with tools and data without messy custom integrations. Implementing the three core building blocks — tools, resources, and prompts — using Python was a great hands-on experience. It’s free on Anthropic’s learning portal. If you’re into building smarter AI workflows, it’s a great place to start. #MCP #Anthropic #Python #AI #LLM #DeveloperTools #ContinuousLearning

AI first will soon be the standard... increase efficiency at least 10-fold, focus on application design, iterate and deliver fast.

I’m excited to share the latest version of NTQR. A Python package for the logic of unsupervised evaluation of classifiers. Want to get started in under 60 seconds? Here is the "Quick Start" flow to get the documentation and interactive notebooks running locally: 1️⃣ Install the package: pip install ntqr 2️⃣ Set up your workspace: Navigate to the folder where you want your tutorial notebooks to live: cd path/to/your/working/directory 3️⃣ Fetch the docs: Run the built-in helper to copy all tutorial notebooks into your current folder: ntqr-docs 4️⃣ Launch & Learn: Open the environment and dive into the examples: jupyter notebook You can see the notebooks at readthedocs.io (NTQR doc page: https://lnkd.in/eugreNDd). The notebooks walk you through the math and the code, making it easy to apply these techniques to your own AI evaluations of classifiers. Give it a spin and let me know what you think! 👇 #Python #DataScience #MachineLearning #AI #OpenSource #NTQR #FormalVerification #AIEvaluation #UnsupervisedEvaluation