Iris Prediction App with Streamlit and Scikit-learn

🚀 K-Nearest Neighbors (KNN) 🤖📊 I’ve just published a new blog on K-Nearest Neighbors (KNN) — one of the most intuitive and beginner-friendly algorithms in Machine Learning. 🔍 In this blog, I’ve covered: - What KNN is and why it’s called a lazy learner - Step-by-step explanation of how KNN works - The math and intuition behind distance metrics - Why feature scaling is crucial (and often ignored!) - Real-world examples for better understanding - Classification vs Regression in KNN - Pros, cons, and practical use cases - Simple Python code to get started 📖 Read the full blog here: 👉 https://lnkd.in/dsn5P7-7 #MachineLearning #DataScience #KNN #ArtificialIntelligence #Python #LearningInPublic #Hashnode #TechBlog #MLAlgorithms #DataAnalytics

Excited to announce the start of my machine learning blog! This will explore a range of ideas, from underlying theory to practical applications, highlighting concepts important for a modern machine learning researcher. First post: Building a multiprocessing DataLoader from scratch. I break down PyTorch's DataLoader class by building a simplified version, focusing on how Python's multiprocessing module enables parallel data loading whilst training the model. You'll see how multiprocessing queues coordinate between worker processes and the main training loop—and why this matters for your training pipeline. Using a toy dataset, I compare single-process vs. multiprocess loading, ultimately showing how even a simple implementation can lead to massive improvements in loading time (over 6 times faster!). Link to the blog: [https://lnkd.in/eg6abKWg] #pytorch #machinelearning #ML #deeplearning #python

Linear regression confused me for weeks. Then I realized it is just drawing the best line through points. That is it. The school equation y = mx + b? That is literally linear regression. I built one from scratch. No fancy libraries. Just numpy and simple Python. If machine learning feels overwhelming, start here. Once this clicks, everything else gets easier. Wrote about my learning journey: Medium: https://lnkd.in/eBvAbdR7 Kaggle notebook: https://lnkd.in/edckUXz2 #MachineLearning #DataScience #Python #Learning

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝗮𝗯𝗶𝘁 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝘀 𝗠𝗮𝗻𝘆 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 Before training any model, always look at a few rows of your data. df.head() You immediately notice: wrong formats unexpected values columns that don’t make sense Many problems are visible in seconds if you simply look at the data first. Two minutes of checking can save hours of confusion later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

Today I built an end-to-end machine learning regression model in Python to predict housing prices from multiple features (square footage, beds, baths, age). The project covers the full ML workflow: • data loading and preprocessing • train/test split • model training with scikit-learn • evaluation using MSE and R² • visualization of actual vs. predicted values Seeing predictions line up closely with real values is always a good reminder of how powerful even simple models can be when the fundamentals are done right. Tools: Python, pandas, scikit-learn, matplotlib #ComputerScience #MachineLearning #DataScience #Python #LearningByDoing

Completed an exploratory project on building a Machine Learning web app using Streamlit and Python. This project was part of the UCS654 Predictive Analytics coursework as a guided exercise and served as an introduction to deploying classical machine learning models through a simple interactive interface. I experimented with models such as SVM, Logistic Regression, and Random Forest, and explored basic performance evaluation using Confusion Matrix, ROC Curve, and Precision Recall Curve. Overall, it was a useful hands on exercise to better understand how ML models can be packaged and exposed through a web application. GitHub Repository: https://lnkd.in/gVttjdZH Live Web App: https://lnkd.in/gpuUY48D #TIET #ThaparUniversity #ThaparOutcomeBasedLearning #ThaparCoursera #Coursera #UCS654_Predictive_Analytics

Matrix Multiplication Basics - Order & Identity (for Machine Learning) Matrix multiplication is a foundational concept in Machine Learning, but it behaves differently from regular arithmetic.   In this infographic, I highlight two key ideas: ● Order matters: in general A x B != B x A ● Identity matrix: multiplying by the identity matrix leaves a matrix unchanged To connect theory with practice, I also include clean Python (NumPy) examples for Jupyter Notebook, showing how these operations look in code.   Understanding these basics makes many ML concepts, from linear transformations to model implementations, much easier to grasp. #MachineLearning #ML #MathForML

👉 Watch here: [https://lnkd.in/gARcrmq8] 🚀 I’ve just uploaded a new YouTube video on NumPy Essentials! If you’re learning Python, Data Science, or Machine Learning, this video will help you strengthen your NumPy fundamentals. 📌 In this video, I’ve covered: • NumPy Data Types • Copy vs View (one of the most confusing concepts for beginners) • Shape & Reshape of NumPy arrays The goal of this video is to explain concepts clearly and practically, especially for beginners and students preparing for interviews and for advance level also. I’d love to hear your feedback 🙌 If you find it useful, feel free to like, comment, or share it with someone learning NumPy. #Python #NumPy #DataScience #MachineLearning #LearningInPublic #PythonTutorial #AI

🚀 Day 1 of my Machine Learning journey Instead of jumping straight into ML models, I decided to start with basics again to build a strong fundamentals. Today was all about NumPy and understanding how data actually works behind the scenes. What I learned today: 🔹 What NumPy arrays (ndarray) really are 🔹 Why NumPy is faster than Python lists 🔹 How data is stored in memory (contiguous arrays) 🔹 Basic operations like mean, sum, and shape One thing that stood out to me: 👉 ML is less about fancy models and more about how you handle data correctly. Taking it step by step and documenting the process. #MachineLearning #Python #NumPy #LearningInPublic #MLJourney

🚀 Excited to share my Machine Learning – Supervised Learning Algorithms repository! From Linear Regression to Naive Bayes, I’ve implemented key supervised learning algorithms with Python. Aimed at anyone looking to learn or explore ML practically. Check out the full code here: 👉 https://lnkd.in/gKyyN9E2 💡 Feedback and contributions are welcome! Let’s learn and grow together. #MachineLearning #Python #AI #ML #DataScience #SupervisedLearning #GitHub #OpenSource