🎥 Here’s a quick demo of my Sentiment Analysis Web Application in action! This project predicts whether a given text is Positive, Negative, or Neutral using Machine Learning. 🔹 Built using Python, TF-IDF, and ML models 🔹 Integrated with a Flask web application 🔹 Deployed live using Render 👉 Try it here: https://lnkd.in/dVU2kzP8 I’ve also shared the project screenshots and code details in my previous post. Would love to hear your feedback! #MachineLearning #Python #Flask #DataScience #Projects #AI

I built my first AI Smart Study Assistant using Python and Streamlit. It’s a simple project where I tried to understand how AI apps actually work with a clean user interface. ✨ What it can do: 📄 Summarize text (demo mode) 🧠 Explain topics in simple words ❓ Generate quiz questions 🎨 Simple and interactive web UI 🛠️ Tech used: Python, Streamlit While building this, I understood how user input flows into logic and how AI-based applications are structured. Right now this is a demo version, but I designed it in a way that it can be upgraded later with real AI models and a chat interface. Next step for me is to improve this project further and keep building more AI-based applications. Would love feedback or suggestions 🙌#AI #Python #Streamlit #MachineLearning #LearningByDoing #ArtificialIntelligence #TechJourney #WomenInTech #DataScience Microsoft Google OpenAI https://lnkd.in/eA-xwtqG

Developed and deployed a content-based Movie Recommendation System using Python and machine learning techniques. The system recommends similar movies by analyzing metadata such as genres, overview, keywords, cast, and crew. It uses text preprocessing, feature vectorization, and cosine similarity to identify related titles, and is presented through an interactive Streamlit application. This project strengthened my practical understanding of recommendation systems, NLP-based preprocessing, feature engineering, and end-to-end ML application development. Tech stack: Python, Pandas, NumPy, Scikit-learn, NLTK, Streamlit Live App: https://lnkd.in/gyzEeKK9 GitHub: https://lnkd.in/gYFHz2Xd #ArtificialIntelligence #MachineLearning #Python #DataScience #RecommendationSystem #Streamlit #ScikitLearn #GitHub #Projects

PyCaret is a low-code Python library that makes machine learning much faster and easier. With just a few lines of code, you can handle preprocessing, compare models, and tune performance in a single workflow. It supports tasks like classification, regression, clustering, and time-series analysis, making it a practical choice for many real-world projects. The book Simplifying Machine Learning with PyCaret by Giannis Tolios is currently available for free: https://lnkd.in/eVFjfGKQ The book guides you step by step through typical PyCaret use cases, from setting up experiments to building, evaluating, and deploying models. It includes practical examples and clear explanations to help you apply PyCaret effectively in real projects. If you want a structured and hands-on introduction to PyCaret, this is a great resource. #machinelearning #python #datascience #ai #pycaret #lowcode #mlworkflow #datatools #analytics #statistics

I built something I'm genuinely proud of this week. 🛠️ Clipper Maker — an AI-powered YouTube clip extractor built entirely in Python. How it works: → Paste any YouTube URL → Whisper AI transcribes the audio with timestamps → librosa scores each moment by energy and speech activity → ffmpeg cuts the top 5–6 clips automatically → Download them individually or as a ZIP What I learned building this: ✅ How to work with audio signals and RMS energy scoring ✅ Running AI models locally with no cloud dependency ✅ Calling command-line tools (ffmpeg) from Python ✅ Building a full web app in pure Python with Streamlit ✅ Structuring a real project with modular, clean code Every line of code written from scratch. Every bug fixed. Every phase tested. This is what learning by building looks like. 💪 🔗 https://lnkd.in/gmqXjgm6 🔗 https://lnkd.in/gWSpczsp #Python #AI #Whisper #BuildInPublic #OpenSource #Developer #SideProject #MachineLearning

🚀 Machine Learning With Python From Scratch Part 3! This one is about something every ML beginner struggles with — One Hot Encoding. Machine learning models only understand numbers. So what do you do when your data has text like "BMW X5" or "Audi A5"? You convert it. One Hot Encoding turns each category into its own column of 1s and 0s. Simple idea, but if you do it wrong your model breaks and most beginners don't even know why. There's also a trap that nobody warns you about, the Dummy Variable Trap. When you have 3 categories, you only need 2 columns. The third one is redundant and adds noise to your model. I cover exactly how to avoid it. In this notebook I cover two ways to do it: pd.get_dummies — quick and simple Sklearn's OneHotEncoder with ColumnTransformer — the proper production way Both approaches are used to predict car sell prices based on brand, mileage and age. 🔗 Full notebook + dataset + detailed explanation on GitHub: 👉 https://lnkd.in/dC5Pzygv Follow along, building this series one concept at a time, from scratch. #MachineLearning #Python #DataScience #OneHotEncoding #FeatureEngineering #GitHub #BeginnerML #100DaysOfCode

🚀 Day 22 of My Generative & Agentic AI Journey! Today’s focus was on Comprehensions in Python — a concise and powerful way to create collections using a single line of code. Here’s what I learned: ⚡ Comprehensions in Python: • Used to create lists, sets, dictionaries, and even generators • Help write logic in a compact and readable way 🧠 Where are they used in real life? • Filtering items → Selecting specific elements from data • Transforming items → Modifying data while creating a new collection • Creating new collections → Generating lists, sets, or dictionaries efficiently • Flattening nested structures → Converting nested data into a single structure 🎯 Purpose of Comprehensions: • Cleaner code → Less lines, more readability • Faster execution → More optimized than traditional loops 💡 Key takeaway: Comprehensions make Python code more elegant and efficient — a must-know concept for writing professional-level code. Moving one step closer to writing optimized and clean Python 🚀 #Day22 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

💡 Prompt Engineering Challenge – Can you pick the best one? Not all prompts are created equal. The way you ask matters just as much as what you ask. 🧠 Drop your answer in the comments — which option is the MOST effective? --- 🚀 Learning side by side: 🐍 Python + 🤖 Prompt Engineering = Powerful combo for the future ✔ Be clear and specific ✔ Give context and constraints ✔ Ask for structured output ✨ Quick insight: Better prompts → Better answers Clarity + context = powerful results Small improvements in prompts can level up your AI skills --- Keep learning. Keep experimenting. Keep improving. #PromptEngineering #Python #AI #Learning #TechSkills #FutureOfWork #AItools #Upskill

Just a developer trying to make life a little more “hands-free.” ✋✨ I built a gesture-controlled mouse using Python and MediaPipe. It wasn’t easy—filtering out hand jitters and detecting a reliable “pinch” in real time took a lot of trial and error. The current setup: 🖱️ Pinch = Left Click 📜 Two-finger lift = Scroll 👍 Thumbs up = Volume Up Still experimenting and improving, but turning an idea into something that actually works feels great. You can also download the .exe file from the Releases section and try it directly. It’s simple, it’s experimental, and I’m learning something new with every line of code 📈 Try it here 👇 👉 https://lnkd.in/g9sZUCjM #CodingLife #SoftwareEngineering #AI #HandsOnLearning #PythonProject #Innovation #BuildInPublic

🚀 Just built a RAG AI Assistant! This tool lets users upload PDFs or text files and get context-aware answers instantly using Python, FastAPI, Sentence Transformers, Groq API, and LLaMA 3.1. Key Highlights: Semantic search for accurate and fast responses Handles multiple document formats Scalable and efficient backend 💻 Check it out: [https://lnkd.in/g9BmUMRD] 📝 Feedback and thoughts are welcome! #AI #MachineLearning #Python #FastAPI #RAG #OpenSource