Jacob Strong’s Post

Thrilled to have completed a challenging and rewarding machine learning project! The journey involved diving deep into data, fine-tuning models, and finding solutions to real-world problems. Every challenge was a learning opportunity, and I’m excited to apply these skills in future projects. Grateful for the support and collaboration of everyone involved! #MachineLearning #ArtificialIntelligence #DataScience #TechInnovation #RealWorldApplication

🐱 Project Spotlight: Image Classification with Support Vector Machine (SVM) 🐶 In this project, I built an SVM model to classify images of cats and dogs from a Kaggle dataset, leveraging this classic machine learning technique to solve an image classification task! Project Overview: 🔹 Data Preparation: Used Kaggle’s cat-and-dog image dataset, resizing and normalizing images for consistency. Extracted key features for SVM, as it works well with lower-dimensional data. 🔹 Feature Extraction: Applied techniques like Histogram of Oriented Gradients (HOG) or SIFT to convert images into feature vectors. These methods capture essential textures and shapes, helping the SVM distinguish between cats and dogs. 🔹 Model Training and Tuning: Trained the SVM with different kernels to find the best fit for image data. Focused on optimizing hyperparameters to improve classification accuracy. 🔹 Results: Achieved a high level of accuracy in distinguishing cats from dogs, proving SVM’s capability for binary classification on visual data! This project is a testament to how SVMs can still perform well for image classification with the right feature extraction approach. A great example of combining traditional machine learning with image processing for effective results! 🚀 #prodigyinfotech

🚦 **Introducing my project (Day 3) : Intelligent Traffic Management System!** 🚦 This project aims to improve traffic flow by using **machine learning** to dynamically manage traffic signals based on real-time vehicle counts, breaking away from the traditional cyclic method. 🛣️ 🔍 How it works: - A vehicle detection model counts the cars in each lane. - The lane with the most cars is prioritized, receiving a green light for **30 seconds**. - The system continuously compares lanes and adjusts green light times, all while running in a loop for **150 seconds** to ensure efficient traffic control. - If any lane hasn’t received a green light by the end of the loop, it gets priority based on a predefined hierarchy. This project was a challenging yet fulfilling experience, where I worked extensively with: - **Python** - **OpenCV** for vehicle detection - **Machine learning models** - Real-time traffic simulations I believe that innovative solutions like this can have a tangible impact on daily life by reducing traffic congestion and making our roads more efficient. 📎**GitHub Repository** : https://lnkd.in/gdu3QDVh Looking forward to applying these learnings in even more advanced systems. 🌟 #TrafficManagement #MachineLearning #OpenCV #AI #Automation #Python #Innovation

🎥 O1 Prompts for Analyzing Images: Harness AI for Visual Insights Explore the revolutionary potential of OpenAI's O1 Prompts to analyze images like never before. Whether you're a creator, researcher, or developer, these tools are designed to unlock a new dimension in visual analysis. 📖 Read more: https://lnkd.in/eND3BqUa 🔍 AI meets creativity and precision! 💡 #O1Prompts #AIForCreatives #ImageAnalysis #OpenAI

🚗 Car Price Prediction Project 🚗 I’m thrilled to share a project I’ve been working on, where I’ve applied Machine Learning to predict car prices based on various features such as mileage, year of manufacture, fuel type, and more. Using a Random Forest Regressor, I trained the model to estimate the selling price with great accuracy. The model was further optimized using GridSearchCV to fine-tune the hyperparameters and achieve better performance. The project involved not only building and training the model but also handling data preprocessing, including scaling numerical features and encoding categorical variables. One of the highlights of this project is the interactive tool I created, which allows users to input car details like mileage, fuel type, and transmission, and get an instant price prediction. The model has demonstrated impressive results with an R-squared score of 0.94, meaning it’s quite reliable when it comes to estimating car prices based on the given features. If you’re interested in learning more about the techniques I used or discussing possible collaborations, feel free to reach out! Always happy to connect with fellow data enthusiasts. #MachineLearning #DataScience #AI #CarPricePrediction #RandomForest #Python #CodexCue

In-depth article on how to create your own real-time object detection model using YOLO with a custom dataset. #ultralytics #YOLO

Happy Friday! Zain Hasan and I noticed that the topic of choosing vector embedding models comes up a lot for builders using vector databases or RAG pipelines. So we spoke about it on video! It was a lot of fun doing it and I got to pick Zain's big brain on the topic. We wanted to make this a casual, approachable conversation with useful, actionable tips, while not being too technical. We discuss things like: - What are embeddings - Differences between models - Where to find benchmarks - How to perform your own evaluations - How to iterate quickly Let us know if you like this & if you want more content in this format. https://lnkd.in/ed28f5Gn

🚀 The Art of Image Processing: Journey into the Depths of Visual World! 🌈 Hello LinkedIn Community! 🌟 With the evolving technology today, image processing has become an exciting exploration area in the world of data science. This time, I invite you to a series of intriguing steps I took in my own Image Processing notebook! 📸✨ In this notebook, we start from basic image processing techniques and delve into complex processes, exploring the depths of visual data analysis at each step. We manipulate images using different methods, diving into the intricacies of visual data. If you want to join this journey and explore the magical world of image processing, you can check out my notebook through the https://tls.tc/beKC3 and learn more about the topic. 📚👩💻 Let's progress, learn, and share together in this colorful world! 🌐💡 #ImageProcessing #DataScience #PythonMagic #Kaggle

🚀 Excited to Share My Task 1: Titanic Survival Prediction 🚀 I’m thrilled to present a project that I’ve been working on recently: Titanic Survival Prediction! 🎥🛳️ In this project, I built a machine learning model to predict the likelihood of survival for passengers aboard the Titanic. Using data from the Titanic competition on Kaggle, I tackled the challenge of estimating survival based on features such as age, sex, passenger class, and fare. 🔍 Key Features of the Project: Data Exploration: Conducted an in-depth analysis and visualization of the dataset to uncover the relationships between different features and survival rates. Feature Engineering: Created and selected impactful features to enhance model performance. Model Building: Implemented various machine learning algorithms, including Logistic Regression, Random Forest, and Gradient Boosting. Evaluation: Assessed the model using metrics like accuracy, precision, recall, and F1-score. 📈 Results: Achieved impressive accuracy scores of over 75% on the validation set. Gained valuable insights into factors influencing survival rates, including passenger class and gender. 🎥 Watch the video below to see a demonstration of the project in action! I’d love to hear your feedback and thoughts on the project. Feel free to connect if you’re interested in discussing machine learning, data science, or similar projects! hashtag #CodSoft hashtag #DataScience hashtag #MachineLearning hashtag #TitanicPrediction hashtag #Kaggle hashtag #ProjectShowcase hashtag #DataAnalysis hashtag #FeatureEngineering

🚀 Excited to share my latest video on classifying Cats and Dogs! 🎥 Join me on an exciting journey into the world of computer vision as we explore the fascinating task of classifying images of cats and dogs. Here's what you can expect: 🔍 Data Exploration: Dive deep into the dataset containing images of cats and dogs, unraveling key insights and patterns. 🛠️ Data Preprocessing: Learn about the essential preprocessing steps taken to prepare the image data for training and testing. 📊 Insights from EDA: Discover interesting trends and characteristics through exploratory data analysis, shedding light on features that distinguish cats from dogs. ⚙️ Feature Engineering: Explore innovative techniques used to extract and engineer features from the images, enhancing the performance of our classification models. 🤖 Model Selection & Training: Delve into the selection and training of machine learning or deep learning models, uncovering the most effective approaches for classifying cats and dogs. 📈💡Results & Discussion: Gain insights into the performance of our models and the implications of our findings for image classification tasks. 🔗Github link: [https://lnkd.in/giz7jReT] Bharatindia #datascience #cats and dogs dataset