Building a Movie Recommendation System with Linear Algebra

A few days back, I shared my first version of a Movie Recommendation System built using cosine similarity and genre-based filtering. At that point, it worked — but only at a basic level. Over the last few days, I tried improving it by: Integrating another dataset (Indian movies). Handling real issues like memory limits and data inconsistency. Moving beyond genres by adding movie overviews. Using TF-IDF to improve similarity. And honestly, one thing became very clear: 👉 Building something is easy 👉 Improving it is where real learning happens.

🌸 GAMS is heading to National Harbor for #INFORMS2026 🌸 We’ll be back at the INFORMS Analytics+ Conference (April 12–14) with a hands-on workshop on building and solving optimization models in Python using GAMSPy, including how machine learning components can be embedded directly into those models. 📌 Bridging Optimization and Machine Learning: An Exploration with GAMSPy 📅 Sunday, April 12 | 1:00–2:45 PM 📍 Room: Camellia 1 Join Steve Dirkse and Adam Christensen for a practical walkthrough of GAMSPy, from core modeling concepts (sets, parameters, variables, equations) through to solving models and working with results in Python. The session also explores how structures like neural networks and regression trees can be incorporated into optimization models. Interested? Register here or DM us with any questions: 👉 https://lnkd.in/dSYEXuJ3 #INFORMS2026 #AnalyticsPlus #OperationsResearch #Optimization #GAMSPy #Python #MachineLearning

🚀 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

Excited to share my latest project — an AI-Based Emotion Recognition System This application can analyze human emotions from voice using machine learning. Users can either record audio in real-time or upload a file to detect emotions like happy, sad, angry, and more. Built using Python, Streamlit, and audio processing techniques, this project reflects my passion for AI and real-world problem solving. GitHub: https://lnkd.in/dmQergwZ I’d love your feedback and suggestions! CodeAlpha #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #Python #Streamlit #AIProjects #EmotionRecognition #SpeechProcessing #AIInnovation #TechProjects #LearningJourney

🚀 Excited to share my first video on Reinforcement Learning! In this project, I built a simple 7×7 grid environment in Python where an agent learns how to navigate from a start point to a goal while avoiding obstacles. Using basic reinforcement learning concepts, the agent improves its decisions over time and discovers the optimal path. This project helped me better understand how agents learn through trial and error which is a fundamental idea behind modern AI systems. Concepts covered: Grid-based environment Agent learning from rewards & penalties Path optimization using Q-learning Visualization of agent behavior This is just the basics of AI & Reinforcement Learning... more advanced projects coming soon 🚀 I’d love your feedback and suggestions! 😊🧡 #AI #ReinforcementLearning #MachineLearning #Python #LearningJourney #ArtificialIntelligence #DeepLearning

🚀 Excited to Share My Machine Learning Project! 🐶🐱 Cats vs Dogs Classification using SVM I recently built a Machine Learning model to classify images of cats and dogs using the Support Vector Machine (SVM) algorithm. This project helped me explore image classification and model optimization techniques. 💡 Key Highlights: 🖼️ Image preprocessing and feature extraction 🤖 Classification using Support Vector Machine (SVM) 📊 Model training and evaluation ⚡ Improved accuracy through parameter tuning 🛠️ Tech Stack: Python | Scikit-learn | OpenCV | NumPy | Matplotlib 🔗 Project Link: https://lnkd.in/gz43DmSG This project enhanced my understanding of machine learning algorithms and computer vision basics. Looking forward to building more AI-powered solutions! 💡 #MachineLearning #Python #ComputerVision #SVM #AI #Projects #Learning

Day 81 - Model Evaluation Today I learned how to evaluate machine learning models to measure their performance. Key concepts: - Classification metrics: Accuracy, Precision, Recall, F1 Score - Confusion Matrix for detailed evaluation - Regression metrics like MAE, MSE, and R² - Importance of choosing the right metric Model evaluation is crucial to ensure models are reliable and effective in real-world applications. git repo https://lnkd.in/gYypHTiV #MachineLearning #ModelEvaluation #DataScience #Python #AI #LearningInPublic

Completed Task 3 – Model Validation & Hyperparameter Tuning in Machine Learning As part of my learning journey, I worked on improving a regression model by analyzing overfitting and applying advanced techniques like cross-validation and hyperparameter tuning. Key Highlights: • Performed overfitting analysis using Decision Tree Regressor • Applied Cross Validation for reliable model evaluation • Used GridSearchCV for hyperparameter tuning • Improved model performance and generalization Tools & Technologies: Python, pandas, NumPy, scikit-learn, matplotlib, seaborn This project helped me understand how to build more robust and reliable machine learning models by balancing bias and variance. Report attached below. #MachineLearning #DataScience #Python #AI #ModelTuning #LearningJourney

🚀 Day 17: Matrix Operations in NumPy Today I worked with matrix operations. ✔ matrix multiplication ✔ transpose ✔ inverse These are very important in: 👉 Machine Learning 👉 Data Science 👉 AI 💡 Key takeaway: NumPy makes linear algebra easy and powerful. #NumPy #Python #AI #MachineLearning

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