Mastering Python with Tkinter, NumPy, Pandas, and AI

Built a Machine Learning obesity prediction app in Python and Flask, reaching 76.6% accuracy. This project was a strong exercise in model building, evaluation, and deploying ML in a practical application. Repo: https://lnkd.in/dnq6kirn #MachineLearning #Python #Flask #DataScience #AI #ModelDeployment #HealthTech

I built an invisibility cloak. With Python. In real time. What started as a fun Computer Vision experiment turned into one of my most technically rewarding projects. Using OpenCV and NumPy — no pre-trained models, no deep learning shortcuts — I built a system that: 🔹 Captures a clean background using median frame averaging (120 frames) 🔹 Detects a person in real time via pixel-level background subtraction 🔹 Applies Gaussian blending + morphological masking for seamless invisibility 🔹 Lets you adjust the effect live — from 0% to 100% invisible The entire pipeline runs on a standard webcam. No GPU required. This project sharpened my understanding of image processing fundamentals — and reminded me that strong logic can create results that feel like magic. Tech Stack: Python · OpenCV · NumPy #ComputerVision #Python #OpenCV #ProjectShowcase #BuildInPublic #MachineLearning #StudentDeveloper

🚀 Day 4 of My Generative & Agentic AI Journey! Today’s focus was on one of the most commonly used data types in Python — Strings. Here’s what I learned: 🔤 Strings in Python: • Strings are immutable — once created, they cannot be changed • Any modification creates a new string instead of changing the original 🔍 Indexing & Slicing: • Accessing individual characters using indexing • Extracting parts of a string using slicing • Learned how powerful slicing is for handling text data 🔐 Encoding & Decoding: • Understood how strings are converted into bytes (encoding) • And how bytes are converted back to strings (decoding) • Important for handling data, APIs, and real-world applications 👉 Key takeaway: Strings are everywhere — from user input to AI models — and understanding how to work with them efficiently is a must. Another step closer to mastering the fundamentals 💪 #Day4 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🚀 Day 52/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 4: KNN Regression Today, I explored K-Nearest Neighbors (KNN) Regression, a simple yet powerful supervised machine learning algorithm used for predicting continuous values. KNN Regression works by identifying the ‘K’ nearest data points to a given input and predicting the output as the average (or weighted average) of those neighbors. KNN is widely used in applications like recommendation systems, pattern recognition, and demand forecasting. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

I’ve been building a machine learning–based approach to extract data from engineering graphs. 📊 The goal is to take graph images (like pressure vs depth) and convert them into structured, usable data instead of relying on manual digitization. I developed a Python pipeline using OpenCV and explored ML-based approaches to improve how curves are detected and separated — including experimenting with U-Net for segmentation and a CNN-based model for prediction.🤖🧠 One of the more challenging parts was getting consistent curve detection and accurately mapping pixel values to real-world units. It took quite a bit of iteration to get the extracted output to closely match the original graph behavior. On the left is the Original Graph, and on the right is the extracted output. I’m really happy with how it’s coming together so far, especially working on something that connects machine learning with a practical, real-world use case.🚀 Tools used: Python, OpenCV, NumPy, Pandas, CNN, U-Net 💻 Sharing a snapshot of the output below 👇 #MachineLearning #DataAnalytics #ComputerVision #Python

I’ve been learning Machine Learning using Python and explored the powerful library Scikit-Learn 📊 Here are some key concepts I covered: 🔹 What is Scikit-Learn? A simple and efficient library for Machine Learning in Python. 🔹 Supervised Learning ✔️ Linear Regression ✔️ Logistic Regression ✔️ Decision Trees 🔹 Unsupervised Learning ✔️ K-Means Clustering ✔️ PCA (Dimensionality Reduction) 🔹 Model Training Steps 1️⃣ Load dataset 2️⃣ Train-test split 3️⃣ Choose model 4️⃣ Train model 5️⃣ Evaluate performance 🔹 Important Functions ✔️ fit() ✔️ predict() ✔️ score() 💡 Learning Outcome: I now understand how to build, train, and evaluate ML models using Scikit-Learn. 📌 Next Step: Working on real-world Machine Learning projects! #MachineLearning #Python #ScikitLearn #DataScience #LearningJourney #AI #Programming

Machine Learning Medical Data using medpy #machinelearning #datascience #medicaldata #medpy MedPy is a medical image processing library written in Python. MedPy requires Python 3. MedPy is a library and script collection for medical image processing in Python, providing basic functionalities for reading, writing and manipulating large images of arbitrary dimensionality. Its main contributions are n-dimensional versions of popular image filters, a collection of image feature extractors, ready to be used with scikit-learn, and an exhaustive n-dimensional graph-cut package. https://lnkd.in/gsBgW5H6

📊 Diving into Linear Regression! Linear Regression is one of the most fundamental algorithms in Machine Learning, used to predict continuous values like housing prices, sales, and more. 🔍 What I learned: ✔️ Understanding the relationship between variables ✔️ Building prediction models in Python ✔️ Evaluating model performance using metrics 💡 It’s amazing how a simple line can uncover powerful insights from data! Currently practicing real-world problems like predicting housing prices 🏡 #MachineLearning #DataAnalytics #Python #LearningJourney #LinearRegression #DataScience

🧠 Day 26 – The 30-Day AI & Analytics Sprint 🌜 🔥 Python Tricky Question! What will be the output of this code? def tricky(n, lst=[]): if n > 0: lst.append(n) return tricky(n-2, lst) return lst print(tricky(5)) print(tricky(4)) ❓ Choose the correct answer: A) [5, 3, 1] [4, 2] B) [5, 3, 1] [5, 3, 1, 4, 2] C) [5, 3, 1] [4, 2, 1] D) Error 💡 Hint: Default arguments in Python are evaluated only once! 👇 Drop your answer before checking the solution! 🚀 Why this matters? This question reveals one of the most common pitfalls in Python: 👉 Using mutable objects as default arguments It can lead to unexpected bugs in real-world applications 😅 #Python #Programming #AI #DataScience #CodingChallenge #30DayChallenge #SoftwareEngineering