Sheikh Rasel Ahmed (Nirob)’s Post

It's fascinating to see how different knowledge we learn can connect unexpectedly! 🤯 Recently, I discovered that the metric I've been using for a project 📊 has a well-established identity in another field. Known as the "Intersection over Union" (IoU) metric, it's popular in object detection within computer vision 👁️🖥️, used to measure the accuracy of predicted bounding boxes against ground truth. The beauty of this is that it can be applied across any field dealing with sets 📈, where defining the intersection and union is possible. This experience is a great reminder to remain curious 🧐 and open 💡. In the vast amount of knowledge, there are always new things to discover and learn. #LifelongLearning #IntersectionOverUnion #DataScience #ObjectDetection #ComputerVision

Proud to showcase two recent projects: K-Means Clustering and Hand Gesture Recognition! 🤖 K-Means Clustering: Grouping data points into meaningful clusters. Hand Gesture Recognition: Using computer vision to interpret hand movements. Learning through these projects has been an amazing experience #DataScience #MachineLearning #KMeansClustering #ComputerVision #HandGestureRecognition #StudentProject #Prodigy

TASK 3 - IRIS FLOWER CLASSIFICATION Thrilled to share the success of my Iris Flower Classification project! 🌸🌿 Trained a smart computer to distinguish between different Iris flower types—setosa, versicolor, and virginica—using their unique sepal and petal measurements. 🌺💡 Leveraging the power of machine learning, we've unlocked a skillful Iris flower identifier. Excited for more adventures in the world of data and machine learning! 🚀🌐 #codsoftinternship #codesoft #DataScience #dataanalytics #IrisClassification #MachineLearningSuccess #FlowerMagic

🚀 Day 9 of the #30DaysOfNumpy Challenge: Unleashing the Artistry of Array Manipulation! 💡💻 Today's leg of the journey delved deeper into the enchanting world of **Array Manipulation**, unveiling even more sophisticated techniques to sculpt and refine our data with precision. 🔗 **Joining Forces:** Mastering the art of joining arrays brought a sense of cohesion to our data. Whether it's stacking vertically, horizontally, or even along a specified axis, the ability to seamlessly merge arrays enhances our control over diverse datasets. 🔀 **Shuffling Strategies:** Today, we explored the dynamic world of shuffling arrays, adding an element of randomness to our data manipulation toolkit. This technique proves invaluable when dealing with tasks like randomizing samples or enhancing the robustness of machine learning algorithms. 🔄 **Masked Arrays:** The power of masking allows us to focus on specific elements of interest while effortlessly ignoring the rest. Today's learning reinforced how masking can be a game-changer in isolating and analyzing critical data points. 💡 **Key Takeaways:** - Joining arrays for enhanced dataset integration. - Shuffling techniques for introducing randomness. - Leveraging masked arrays to focus on essential elements. 🚀 **Challenge Progress:** - Days Completed: 9/30 - Proficiency Gained: 🎓🚀 👉 **What's Next?** As we bid adieu to Day 9, the #30DaysOfNumpy Challenge continues to propel us towards expertise in array manipulation. Each day brings new insights and skills, making the journey an exhilarating exploration of data possibilities! 🚀🔧 #NumpyChallenge #ArrayManipulation #DataScience #NumPy #CodingJourney #LearningIsContinuous

🌟 Excited to share my latest achievement! 🌟 I have successfully completed a webinar on "Linear Regression in Machine Learning" conducted by TOPS Technologies. This insightful session has enhanced my understanding of linear regression techniques and their applications in machine learning. Grateful for this learning opportunity and looking forward to applying this knowledge in real-world projects. #MachineLearning #LinearRegression #TOPSTechnologies #ContinuousLearning #ProfessionalDevelopment ✨

Hey LinkedIn community, 🌟 Day 67-68 Update: Delving into XGBoost and Image Processing! The past couple of days have been all about deepening my understanding of how XGBoost works in both classification and regression settings. It's truly an incredible algorithm, and just going through its research paper makes you appreciate the power behind it. 📚 In parallel, I've been diving into a new challenge: my semester's mini-project. For the first time, I'll be exploring the world of image processing, stepping beyond the realm of datasets like MNIST. I won't lie – it's been tough navigating through this new territory, but the learning curve is exhilarating. 🚀 Excited to see where both these journeys lead me – from mastering XGBoost to unraveling the complexities of image processing. Stay tuned for more updates on my progress! #ImageProcessing #MachineLearning #DataScience

🚀 Completed the Striver Graph Series! 🚀 This summer, I delved deep into the world of graphs, solving 54 challenging questions that enhanced my understanding of topological sort, disjoint sets, and various graph algorithms. A big thanks to Raj Vikramaditya for the fabulous graph series. This experience has been incredibly rewarding and has significantly sharpened my problem-solving skills. #GraphTheory #Algorithms #LearningJourney #SoftwareDevelopment #takeuforward

Codesoft_Tast 1:Excited to share my latest project: Titanic Survival Prediction!! I analyzed Historical data to develop a model predicting survival probabilities based on key factors. 📊 This project prediction involves using machine learning techniques to analyze and predict the likelihood of passengers surviving the Titanic disaster based on various features such as age, gender, class, and embarkation point. By training a model on historical data, the goal is to create a predictive algorithm that can estimate the probability of survival for individuals on the Titanic, providing insights into factors that influenced outcomes during the tragic event.Thrilled with the results and the insights gained.#MachineLearning #DataScience #TitanicSurvivalPrediction #codsoft github link -https://lnkd.in/gbq4AWVx

🌟 Excited to share an insightful blog post on "End-to-end Kernel Learning via Generative Random Fourier Features"! The post discusses a one-stage process that unifies kernel learning and linear learning, leading to superior generalization performance. The method’s enhanced adversarial robustness is also explored and verified. Check out the full blog post here: https://bit.ly/3vDIkmp #MachineLearning #DataScience #ArtificialIntelligence

Project Completion - Iris Dataset Prediction 🌟 Happy to share that I've successfully completed a project on predicting species in the Iris dataset! 🏵️ Leveraging machine learning algorithms, I achieved high accuracy in classifying the different Iris species. Grateful for the learning journey and looking forward to applying these skills to new challenges✨! CodSoft #MachineLearning #codsoft #DataScience #IrisDataset