Mastering Image Cropping with NumPy and ROI Extraction

Professional & Technical (Best for showcasing skills) Headline: Automating Image Recognition with TensorFlow 🌸 I recently worked on a flower classification project using the tf_flowers dataset. This project allowed me to dive deep into the mechanics of computer vision and efficient data handling. Key highlights of the implementation: Data Pipeline: Utilized tensorflow_datasets for a seamless 80/20 train-test split. Preprocessing: Implemented a custom preprocess function to normalize pixel values and resize images to $150 \times 150$, ensuring consistency across the neural network. Prediction & Validation: Built a visualization loop using Matplotlib to compare the model’s predicted labels against actual data points. It’s exciting to see how a few lines of Python can transform raw pixel data into accurate classifications. Looking forward to refining this further with transfer learning! #TensorFlow #DeepLearning #ComputerVision #Python #DataScience #MachineLearning

Most people use keyboards every day — but very few think about how typing efficiency can actually be measured. I built a machine learning model to predict typing time between key pairs (bigrams) based on keyboard layout and key positions. By applying feature engineering and model optimization, I reduced prediction error (MAE) from 174.8 ms to 94.4 ms — a 45.9% improvement. This project helped me understand how data-driven approaches can be used to evaluate and improve real-world user interactions. Next, I’m working on turning this into a simple usable application. GitHub: https://lnkd.in/gbHiKdGu #MachineLearning #Python #AI

A House Price Prediction Model using Linear Regression! I recently put together a complete, end-to-end Google Colab notebook demonstrating how to predict real estate prices using machine learning. It's fascinating to see how math and data come together to model real-world markets. Data Preprocessing: Handling missing values, feature scaling, and encoding. Model Building: Training a Linear Regression model from scratch using Scikit-Learn. Evaluation: Analyzing the model's performance using metrics like RMSE and R-Squared to ensure accuracy. The biggest takeaway for me was realizing how heavily feature engineering impacts the final model's accuracy. Grateful to IncodeVision for giving me a project that pushed me beyond just writing code! #MachineLearning #DataScience #LinearRegression #Python #GoogleColab #RealEstateTech #IncodeVision

🚀 Excited to share my AI & ML Practicals Repository! I’ve uploaded my Artificial Intelligence and Machine Learning (AIML) lab practicals on GitHub, covering key concepts like data preprocessing, EDA, supervised & unsupervised learning algorithms, and model evaluation. 🔍 This repository reflects my hands-on learning in Data Science and helps strengthen my practical understanding of machine learning concepts :🔹 DataFrame Operations 🔹 Correlation Matrix 🔹 Normal Distribution 🔹 Simple Linear Regression 🔹 Logistic Regression 🔹 Decision Trees (ID3 Algorithm) 🔹 Confusion Matrix 🔹 Decision Tree Pruning A special thanks to my mentor Ashish Sawant for guiding us throughout this practical sessions !! 📂 Check it out here : https://lnkd.in/g7f3Q8vv I’d love your feedback and suggestions! 😊 #MachineLearning #ArtificialIntelligence #DataScience #Python #GitHub #LearningJourney

 EDA & Feature Engineering 📊 Garbage in = Garbage out. That's why EDA comes first. Before you touch any ML model, you need to understand your data. EDA = Exploratory Data Analysis ✅ Check shape, types, nulls ✅ Plot distributions is it skewed? ✅ Find correlations -heatmaps reveal hidden patterns ✅ Spot outliers before they ruin your model Then comes Feature Engineering — turning raw columns into model fuel: → Encoding categories → Scaling numbers → Creating new features → Dropping irrelevant ones I'm learning this at Humber College, Ontario while building real ML projects 🎓 What's your go-to EDA library -- Pandas Profiling, Seaborn, or Plotly? #EDA #FeatureEngineering #MachineLearning #DataScience #Python #HumberCollege

6 months ago I had no idea what a "state graph" was. Today I just finished writing complete LangGraph notes — from zero to production-ready agents. The thing nobody tells you about LangGraph: It's not about AI. It's about state machines. Once that clicks, everything else is easy. My notes cover the mental models, not just the syntax. Because syntax you can Google. Thinking like an engineer — that takes time. Sharing the full PDF with anyone who needs it. What topic should I cover next? 👇 #LangGraph #AIEngineering #Python #LearningInPublic #AIAgents #SoftwareEngineering

45 Days ML Journey — Day 12: Support Vector Machine (SVM) Day 12 of my Machine Learning journey — diving into Support Vector Machine (SVM), a powerful algorithm used for both classification and regression tasks. Tools Used: Scikit-learn, NumPy, Pandas What is SVM? SVM is a supervised learning algorithm that finds the optimal hyperplane to separate data points of different classes with the maximum margin. Key concepts: Hyperplane : Decision boundary that separates classes Margin : Distance between the hyperplane and closest data points Support Vectors : Critical data points that define the boundary What if data is not linearly separable? SVM uses the Kernel Trick to transform data into higher dimensions where it becomes separable. Common kernels: Linear Kernel Polynomial Kernel RBF (Radial Basis Function) Kernel Why use SVM? Effective in high-dimensional spaces Works well with clear margin of separation Versatile with different kernel functions Code notebook: https://lnkd.in/gi_4TqUb Key takeaway: SVM is a robust algorithm that focuses on maximizing the margin, making it highly effective for complex classification problems. #MachineLearning #DataScience #SVM #Python #ScikitLearn #LearningInPublic #MLJourney

Built Linear Regression models from scratch using Gradient Descent (single-variable → multi-variable). Instead of using libraries like sklearn, I implemented the core components manually: - Cost function (Mean Squared Error) - Gradient computation - Parameter updates using gradient descent I started with a single-variable model and then extended it to handle multiple features such as size, number of bedrooms, and age of the house. The attached visualization shows: - Actual data points - Model predictions - How the model learns the relationship between features and price Key takeaways from this project: - Understanding how gradient descent updates parameters step by step - Importance of learning rate in convergence - How multiple features influence predictions in real-world scenarios #MachineLearning #AI #Python #GradientDescent #LinearRegression #LearningInPublic

🚀 Project Launch: MNIST Image Classifier (Handwritten Digit Recognition) I’m excited to share my latest Machine Learning project — an MNIST Image Classifier that can accurately recognize handwritten digits from images. 🧠 What the model does: • Takes an image of a handwritten digit (0–9) • Processes and normalizes pixel data • Predicts the correct digit using a trained ML model 📊 Key Highlights: • Trained on the MNIST dataset • Built an end-to-end ML pipeline (data preprocessing → model training → evaluation) • Achieved high accuracy on handwritten digit recognition 💡 Tech Stack: Python | NumPy | Scikit-learn / TensorFlow | Computer Vision 🖥️ Application: Developed a simple and user-friendly interface to test predictions in real time. This project helped me strengthen my understanding of image classification, data preprocessing, and building practical ML systems. I’d love your feedback! 🙌 #MachineLearning #ComputerVision #AI #Python #DeepLearning #StudentProject

Day 5 of my Machine Learning Journey 🚀 Today I worked on one of the most important concepts in data preprocessing — Encoding & Feature Scaling. 🔹 Converted categorical data into numerical using LabelEncoder 🔹 Applied Standardization using StandardScaler 🔹 Applied Normalization using MinMaxScaler 🔹 Practiced on multiple datasets (COVID, Tips, Insurance) Understanding how to properly prepare data is crucial before applying any ML model. This step directly impacts model performance. Learning step by step and building strong fundamentals 💪 #MachineLearning #DataScience #Python #LearningJourney #DataPreprocessing #AspiringDataScientist