beau’s Post

Week 2: 1. Finished up with Data Structures and Algorithms - the exam was surprisingly easier than I thought. Should start doing some LeetCode easy questions to get in the habit of consistently coding 2. Excited (and a bit nervous) about taking an Intro to Deep Learning course next semester. Might be beyond my capabilities at the moment - will find out real fast after the first couple of lectures 3. After sending out loads of applications, I finally got an internship opportunity! Contract not signed yet, but I'm hopeful and looking forward to it, hopefully no hiccups. 4. Took a little break to explore the Netherlands and looking forward to kicking off the next semester.

🚀 Excited to share the results of my latest project during my Prodigy internship! 🌟 Through individual effort and dedication, I successfully implemented a linear regression model to predict house prices based on key features like area, number of bedrooms, and bathrooms. 🏡 Leveraging Python's scikit-learn library, I conducted thorough evaluations and visualizations, showcasing the model's accuracy and effectiveness in real estate prediction tasks. Thrilled to contribute innovative solutions to the team and continue making strides in the world of machine learning! #MachineLearning #DataScience #ProdigyInternship 🎓💼

During my internship at AIMER society .Just wrapped up an intriguing project utilizing YOLO in Google Colab for object detection in traffic scenarios. The process was both challenging and rewarding, delving deep into the world of computer vision. Leveraging YOLO's capabilities in a cloud-based environment like Google Colab opened up a world of possibilities. From training the model to fine-tuning for accurate detections, every step was a learning experience. The ability to analyze traffic patterns using YOLO showcased the power of this cutting-edge technology. Excited to share my journey in computer vision and the insights gained through this project. Looking forward to exploring more applications and advancements in the field. Stay tuned for further updates on my progress in the realm of object detection and computer vision. #AIMERS #AIMERSOCIETY #APSCHE #YOLO #GoogleColab #ObjectDetection #ComputerVision #TrafficAnalysis #MachineLearning #AI#TechEnthusiast

🚀 Exciting News! 🚀 🎉 I'm thrilled to share that I've successfully completed a data science project as part of my internship with Coding Samurai! 🎉 📊 Project Overview: Title: Predicting House Prices Description: Developed a machine learning model to predict house prices based on various features such as square footage, number of bedrooms, and neighborhood using regression analysis techniques. 🔍 Key Steps Implemented: • Data Collection from Amsterdam Housing Dataset 🏠 • Data Exploration and Preprocessing 📊 • Feature Selection and Model Training 🛠️ • Model Evaluation and Performance Optimization 📈 • Documentation and Presentation of Findings 📝 💡 What I've Learned: Through this project, I've gained hands-on experience with regression analysis, feature engineering, model selection, hyperparameter tuning, and data visualization techniques. It's been an incredible learning journey, and I'm excited to apply these skills to future projects! 📷 Check out the video below to see a screen recording of the code implementation and its outputs in action. 🔗 GitHub Repository: https://lnkd.in/gkC7MjKG 🚀 Thank you Coding Samurai for providing this valuable learning opportunity, and I'm looking forward to more exciting projects ahead! #DataScience #MachineLearning #CodingSamurai #Internship #ProjectCompletion

🔍🔒 CodeClause Internship: Fraud Detection with Decision Trees 🔍🔒 Excited to share insights from my internship project on Fraud Detection using Decision Tree Classifier: Focus: Detecting fraudulent credit card transactions. Dataset: Credit card transactions by European cardholders (Sept. 2013). Features: Time, PCA-transformed components (V1-V28), Amount, Class. Exploration: Explored dataset, prepared it for model training. Model: Trained Decision Tree Classifier. Evaluation: Achieved:Accuracy: 99.92% Precision: 75.96% Recall: 80.61% F1-Score: 78.22% Matthews Coeff.: 0.78 Conclusion: Decision Tree Classifier shows promise. Opportunities for optimization and addressing dataset imbalances exist. #codeclauseinternship #codeclause #datascience #jupyternotebook #recruitmentjobs #python #ai

We built the M.AR.C.I.E engine for specific use cases for #fiction #writers that can be quickly adapted to adjacent spaces. This prevents over investing in grand scenarios, produces better results, and is very cost friendly to run. Exact opposite of issues mentioned in this article. We acquired funding through NextCorps. We are excited to partner with University of Rochester Data Science program for paid internship this summer. #storytelling is as old as the human civilization. We are excited to deploy AI in this highly fragmented, multibillion dollar long-form commercial space. https://lnkd.in/g8Ak6MWV

I am thrilled to announce that I have successfully completed Task 1, titled "MOVIE GENRE CLASSIFICATION" where I created a machine learning model that can predict the genre of a movie based on its plot summary or other textual information using techniques like TF-IDF and word embeddings with classifiers such as Naive Bayes and Support Vector #MachineLearning #Internship #Codsoft

Thrilled to share my machine learning endeavor! Utilized Google Colab and Scikit-Learn with preprocessed House Price datasets. Implemented linear regression for cluster determination, vividly visualizing patterns and clusters in an illuminating 2D format. Exciting times in the realm of machine learning exploration! #machinelearning #prodigyinfotech #internship

As I prepare to wrap up my one-month internship at the Danish Technological Institute's Big Science Department, I reflect on my contributions and the invaluable experience gained. The project I was assigned involved treating synchrotron beamline data, so that it could be compatible with internal software. Recognizing the complexity of the task, I made the strategic decision to develop a Python-based API. This API is currently designed to convert raw data from a specific diffraction experiment carried out at one beamline into a required format. This process involved not just bridging the gap between raw measurement data and software processing capabilities, but also performing complex data treatment, background subtraction, and peak fitting of the diffraction data. In essence, this work has allowed the Big Science team to seamlessly treat previously incompatible datasets with the internal EASI-STRESS software. A highlight of my experience is the scalable nature of the code I built. Although it currently works for a specific type of data from one beamline, it's designed to be easily expandable and adaptable for the conversion of other datasets from different beamlines. As I look ahead to my future endeavors, applying the skills and insights I’ve gained, I extend my heartfelt gratitude to the Big Science team at the Danish Technological Institute for their guidance and support. #internship #datascience #python #diffraction #bigscience #danishtechnologicalinstitute

🌟 Excited to Share My Project Experience! 🌟Thrilled to unveil the project I worked on during my internship at Innovate Intern - "Deep Learning Approach to Sign Language Recognition for Real-Time Connection." During this immersive experience, I delved into the fascinating realm of deep learning, applying Python for coding, SQL for data management, and utilizing statistical methods to enhance our models. I'm immensely grateful to the Innovate Intern team for their guidance and support throughout this journey. This project has not only honed my technical skills but also ignited a passion for leveraging technology to foster inclusivity and accessibility.Looking forward to applying these learnings in future endeavors! 🚀#Internship Project #DeepLearning #Python #SQL #Statistics #Innovation #Accessibility