Yassin Abdulmahdi

🚀 Excited to share my latest project on web scraping! 🚀 I recently developed a web scraper for the Wuzzuf website, extracting valuable job data and insights. This project allowed me to deepen my understanding of: 🖥️ Web scraping techniques using Python 🔍 Navigating dynamic content and handling HTML structures 🛠️ Working with libraries like BeautifulSoup, requests, and more 📊 Organizing and analyzing large datasets This tool is designed to automate job data extraction, making it easier to stay updated on the latest opportunities in Egypt's job market. Looking forward to applying these skills to more real-world projects and exploring new challenges in data analysis and automation! #WebScraping #Python #DataAutomation #JobMarket #DataAnalysis

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Hey everyone! Alhamdullah I've graduated from Computer Science major. And of course I had to do a graduation project, well I did (we did). I want to talk a bit briefly about the idea and go into details of the Mobile application! Sagor... is the name of the our project, it refers to a valley in beloved Palestine. The idea was to make an automated IoT system along with an AI to monitor and control the Fish farms's Pools. We aimed to monitor the environment of each pool in the farm by looking after the pH, temperature and the Oxygen levels. For the Controlling part, we provided a Feeding pump that dispatches the appropriate amount of food into the pools. That's the abstract of the idea, there are a lot to talk about, but I want to focus on the Mobile application and how did we manage to use the Android technology to provide a friendly-app to be used as a dashboard the Farm's guard (Client). We thought of providing insights of the client's pools by visualizing the changes of the environment's characteristics (pH, Temperature, Oxygen also but we couldn't afford it) Here comes the challenge, the idea is easy to be done but I thought of challenging myself and facing my Android's only fear (Canvas). I implemented the app using only Compose Canvas API to visualize the pools' data, no third-party libraries for the UI. It was one of the most exciting challenges I had. I found out that it's very hard to maintain a clean code when dealing with Canvas, this was a point I looked after when dealing with the project. The app had other features but I wanted to mention the Canvas part only since it's not that famous to implement an entire app based on it only! I would like to thank engy osama our UI/UX designer for such a challenge :-: Here's the GitHub repository if anyone would like to take a look! https://lnkd.in/ddMTZwnm

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🚀 Excited to share my latest project: Sentiment Analysis of user reviews on the Dana app! Using deep learning models, I've analyzed 139,500 reviews to classify sentiments as negative, neutral, or positive. Tackling data imbalance, optimizing model performance, and leveraging cloud-based GPUs were key challenges I overcame. Explore the details and see the impact of AI in understanding user feedback: https://lnkd.in/guP8XHde #MachineLearning #AI #SentimentAnalysis #DataScience #DeepLearning

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that's new certificate of my latest 45 days long Internship about Artificial intelligence at 1stop from personifwy In this period of time i learn about object detection and facial feature detection with tensorflow and keras. #AI #tensorflow #keras #machinelearning

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𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 1: 𝐍𝐮𝐦𝐏𝐲 In the first Python Libraries Session of atomcamp's Data Science and AI bootcamp, we covered NumPy, which is a Python library for scientific computing. Maham Farooq led an extensive session that provided an overview of NumPy and covered several functionalities offered by the library. Some of the concepts we covered were: 𝐍𝐮𝐦𝐏𝐲 𝐀𝐫𝐫𝐚𝐲𝐬: NumPy arrays are better and more powerful version of Python lists. They enable you to perform fast calculations and are at the core of all the features provided by NumPy. Unlike Python lists however, you can only put data of the same type in the array, and does not dynamically increase it's size. NumPy provides a host of array initialization functions like array, arange, linespace, etc. 𝐀𝐫𝐫𝐚𝐲 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: NumPy arrays can be indexed in the same way as Python lists, but for array with 2 dimensions and above, instead of using seperate brackets to specify the indexes of different dimensions, you can use one brackets and use commas to seperate the indexes like this arr[1, 2, 3]. This example is equivalent to arr[1][2][3]. NumPy arrays also support slicing in the same way as Python lists. 𝐀𝐫𝐫𝐚𝐲 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐬: NumPy provides a wide range of attributes for NumPy arrays. Some of the attributes we covered were: ● ndarray.shape: Returns a tuple consisting of the dimensions of the array. For 1D arrays, it just returns the number of elements in the array. ● ndarray.ndim: Returns the number of dimensions of the array. ● numpy.itemsize: Returns the length of each element of array in bytes. ● numpy.append: Appends values along the mentioned axis at the end of the array. ● numpy.reshape: Reshapes the array with respect to the given dimensions. 𝐄𝐥𝐞𝐦𝐞𝐧𝐭-𝐰𝐢𝐬𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: NumPy array also provides the useful feature of element-wise operations. You can perform addition, multiplication, division and subtraction between each element of two NumPy arrays. You can just write the desired operator between the names of the two array like this arr1 + arr2, and NumPy will handle the rest! The session was great learning experience and we covered some of the many useful functionalities of NumPy. Maham Farooq, again, did an excellent job in explaining complicated concepts in a simple manner. Looking forward to other sessions! #DataAnalysis #DataScience #Python #NumPy #Bootcamp #atomcamp

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🌟 Thrilled to announce that I've just completed the "End-to-End Machine Learning" course on DataCamp! 🚀 This course provided me with comprehensive insights into the entire machine learning process, from data preprocessing to model deployment. I gained hands-on experience with various algorithms, tools, and techniques that are essential for building and deploying robust machine-learning models. I'm excited to apply these new skills to real-world projects. 💻📊 #MachineLearning #MLOps #CI_CD #Deployment

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💻 𝔼𝕩𝕡𝕝𝕠𝕣𝕚𝕟𝕘 ℕ𝕖𝕩𝕥.𝕛𝕤 𝕋𝕠𝕘𝕖𝕥𝕙𝕖𝕣: 𝔸 𝕁𝕠𝕦𝕣𝕟𝕖𝕪 𝕠𝕗 𝕋𝕖𝕒𝕞𝕨𝕠𝕣𝕜 𝕒𝕟𝕕 𝕃𝕖𝕒𝕣𝕟𝕚𝕟𝕘 💻 I’m excited to share the progress on ＬＥＡＲＮＩＣＡ, a course management website that I’m developing as part of the Endgame phase in the Programming Hero course. This project isn’t just about learning new technologies like 𝙉𝙚𝙭𝙩.𝙟𝙨—it’s also about teamwork, collaboration, and mutual growth. 𝙉𝙚𝙭𝙩.𝙟𝙨 is new for all of us on the team, and that’s what makes this journey so special. We’re constantly learning from each other. When I face an issue—whether it’s related to server-side rendering or deployment—my teammates are always ready to step in and help. And when they encounter roadblocks, I’m there to support them, too. It’s this cooperative spirit that makes our progress possible. 🙌 𝙉𝙚𝙭𝙩.𝙟𝙨 has been an exciting tool, offering features like static site generation and automatic routing that simplify complex tasks. But as with any new technology, we’ve faced our share of challenges. Thankfully, no one is in it alone—we troubleshoot together, share ideas, and grow through each other’s experiences. Working in a team where everyone is so supportive has made this learning process smoother and far more enjoyable. It’s amazing what we can achieve when we work together toward a common goal! #Nextjs #Teamwork #Learnica #ProgrammingHero #WebDevelopment #EndgameJourney #FullStack #Collaboration

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Hi connecters So happy to share with you my internship experience in ecx_edteh industry with co_ founder Mr. Koushik Vedula and the founder Mr. GCV Srikar We are learning how to data handling in python using panda's

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❤️اللهم إني أتبرأ من حولي وقوتي والتجأ إلى حولك وقوتك❤️ I would like to share my presentation about Data Presentation: Visualization in ML at Higher Institute for Applied Sciences and Technology. The Objectives of this presentation are: 1- Model structure visualization 2- Visualizing performance metrics 3- Comparative model analysis 4- Feature importance visualization 🌹enjoyable reading🌹 #ai #ml #seminar #research #visualization

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I am excited to share with you my first time working with data science projects. This is my first-time handling data science projects datasets in general. 👩‍💼 Adult Income EDA I worked on preprocessing an adult Income dataset to prepare it for analysis and modeling. Here's a snapshot of the process: Handling Missing Values: Imputed missing data for crucial columns. Plotting Relations: Plotter relations between features for ease of understanding. Feature Engineering: Grouped categorical variables to simplify analysis. Data Visualization: Plotted various relationships within the dataset to uncover insights and patterns. Correlation Analysis: Conducted correlation analysis to understand the relationships between different attributes and income. Check out the detailed preprocessing steps and code on my GitHub:

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2nd Call for Papers: The 1st Workshop on NLP for Languages Using Arabic Script (AbjadNLP 2025) Co-located with COLING 2025 Conference, Abu Dhabi, UAE (19-20 January 2025) Workshop: https://lnkd.in/dGtfYgEA Submission URL: https://lnkd.in/dMVtEWKr > AbjadNLP 2025 aims to advance innovation in Natural Language Processing (NLP) for languages using the Arabic script, focusing on Abjad and Ajami languages. > Ajami languages, representing a myriad of African languages that have adopted the Arabic script, span at least 43 distinct languages, including Hausa, Fulfulde, Mandingo, Swahili, Wolof, Kanuri, Twi (Akan) Bambara and Tamazight among others. The combined number of speakers of these languages is estimated to exceed 200 million within Africa alone. Topics of interest are: > Core Technologies > Applications > Resources and Tools > Cultural and Sociolinguistic Considerations >> Submission Guidelines: We follow the COLING 2025 standards for submission format and guidelines. Submissions should conform to the following types: > Long papers: Up to eight (8) pages, presenting substantial, original, completed, and unpublished work. > Short papers: Up to four (4) pages, describing a small focused contribution, negative results, system demonstrations, etc. >> Key Dates: Paper Submission Deadline: 15 November 2024 Notification of Paper Acceptance: 6 December 2024 Camera-ready Paper Deadline: 13 December 2024 Workshop Date: 19 or 20 January 2025

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الحلقة ال 24 من بودكاست إلى البيانات و ما بعدها مع م. يونس أبو النجا Younes Abouelnagah على اليوتيوب و تقدر تشوفها من هنا: https://lnkd.in/dntdkSMK اتكلمنا ازاي الشركات الكبيرة ب Scale Machine Learning Systems عشان تقدر تخدم ملايين من المستخدمين و غطينا النقط دي: 1. Introduction to Scaling ML Systems: - Why is scaling important in the context of machine learning? 2. Challenges in Scaling: - What are the common challenges organizations face when scaling their machine-learning systems? - How do these challenges differ from scaling traditional software systems? 3. Data Scaling: - How do the volume, velocity, and variety of data impact the scaling of machine learning systems? - What strategies can be employed to handle large and diverse datasets efficiently? 4. Model Scaling: - As models become more complex, what considerations are crucial for scaling machine learning models? - How do we decide whether to scale up (bigger models) or scale out (distributed computing)? 5. Infrastructure and Resources: - The roles infrastructure plays in scaling ML systems, and how do resource considerations change with scale 6. Scaling Training Pipelines: - How can organizations scale their model training pipelines to handle large datasets and complex models? - What are some best practices for distributed training in machine learning? 7. Operational Considerations: - Once a model is trained, what operational challenges arise when deploying and managing it at scale? - How do you address issues such as monitoring, logging, and maintaining model performance in production? 8. Scalability and Performance Metrics: - What key metrics should organizations track to ensure the scalability and performance of their machine-learning systems? - How do these metrics evolve as systems scale? 9. Data Privacy and Security: - How does scaling impact data privacy concerns, and what measures should be taken to ensure secure scaling of ML systems? - Are there specific considerations for handling sensitive information at scale? 10. Future Trends in Scaling ML: - What emerging technologies or methodologies do you see influencing the future of scaling machine learning systems? - How are advancements in hardware, software, or algorithms shaping the scalability landscape? 11. Advice for Practitioners: - Advice for individuals or teams looking to scale their machine-learning systems - Are there common pitfalls or misconceptions that should be avoided? اتمنى الحلقة تكون مفيدة و حابب اشكر جدًا م. يونس على وقته و المعلومات اللي شاركها معانا.

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🌟 Embarking on a New Journey: Data Science with Python 🌟 📊 The Power of Data is Unstoppable In a world driven by information, data is the new oil, and understanding it is crucial to unlocking endless opportunities. Today, I'm thrilled to share that I've taken my first step into this exciting world by completing the "Introduction to Python" course as part of the Associate Data Analyst with Python certification from DataCamp. 🚀 From Code Novice to Data Enthusiast Diving into Python has been an eye-opening experience. As someone new to data science, I'm fascinated by how coding can transform raw numbers into actionable insights. This course has equipped me with the foundational skills in Python which I would need to become a data guru, and I'm excited to build on this knowledge as I progress through the certification. 🔮 Looking Ahead: My journey has just begun, and my goals are clear: to delve deeper into data analysis, master the tools that can turn data into stories, and ultimately contribute to making data-driven decisions in any industry I step into. I’m eager to learn, grow, and embrace the challenges ahead. Thank you to DataCamp for providing such an excellent platform for learning. Here’s to many more milestones on this data science journey! 🚀 #DataScience #Python #LearningJourney #DataCamp #ContinuousLearning #DataAnalysis

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My conversation with Javad S. (products manager of Nafun International) series of videos about the ups and downs of artificial intelligence. STAY TUNED #AI #AI_hallucination

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if a project needs 1 week to be completed, implementing it with #visual_studio is gonna take 2 weeks, 1 for writing code and 1 for waiting for vs to unfreez

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🎓 New Certificate: Miuul Feature Engineering 🎉 I am excited to share that I have successfully completed the Feature Engineering module of the Miuul Machine Learning Bootcamp! During this training, I developed essential skills necessary for data science, particularly in the context of Big Data and Artificial Intelligence projects. Key topics I learned include: · Outliers · Missing Values · Encoding · Scaling · Feature Extraction This training provided me with a great opportunity to enhance my competence in data science and improve my ability to conduct data analysis in my projects. I am particularly excited about how these skills relate to machine learning, as effective feature engineering is crucial for building robust models. At the end of the training, we developed a model to predict diabetes in Pima Indian women using data collected by the National Institute of Diabetes and Digestive and Kidney Diseases in the USA. Before developing the model, we applied data preprocessing and feature engineering steps on the raw data to make the model more effective. I would like to thank Vahit Keskin, Nimet Karagöz, Dilara Alcan, and the entire Miuul team for their excellent mentorship and lessons. I eagerly look forward to gaining more knowledge in the field of data science and applying what I have learned! 💡 #DataScience #FeatureEngineering #MachineLearning #Miuul #DataAnalysis #Certificate

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Titanic Survival Prediction Model I am thrilled to share a project I worked on during my Data Science internship at Encryptix . 🔺Project Overview: -Dataset: The Titanic dataset, which includes details about passengers such as age, gender, ticket class, fare, cabin, and survival status. -Objective: To develop a machine learning model that accurately predicts the survival of passengers based on the provided features. 🔺Key Learnings: -Data preprocessing and feature engineering to handle missing values and categorical data. -Implementation and evaluation of various machine learning algorithms. -Model optimization to improve prediction accuracy #Encryptix #Datascience #Machinelearning #Internship

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🌟 Big News! 🌟 🚢 Just finished another cool project at Afame Technologies as a Data Science Intern, and I'm super excited to share what we've achieved! 📊 We used Power BI to explore the Titanic Survival Prediction dataset, digging into the data to find out who survived and why. 🛳️ We looked at things like age, gender, and ticket class to see what made a difference in survival. 💡 By organizing data, making charts, and predicting outcomes, we uncovered some really interesting stuff about the Titanic disaster. 📈 This project taught us a lot about analyzing data and how it applies to real life. 🎉 Huge thanks to the team at Afame Technologies for their help and support. This internship has been an amazing learning experience! 🚀 Now, I'm pumped to take on more projects and keep learning in the world of Data Science! 🌐 https://lnkd.in/geCctNkE #DataScience #PowerBI #TitanicSurvivalPrediction #AfameTechnologies #DataAnalytics #InternSuccess #DataViz

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Hey everyone, I'm excited to share my latest project on restaurant review classification, now available on GitHub! This project showcases a powerful model developed from scratch using advanced Natural Language Processing (NLP) techniques to classify restaurant reviews effectively. Key Features and Benefits: From Scratch Development: Built the NLP models from the ground up, ensuring a deep understanding and customization of the solution. Accurate Classification: Efficiently classifies restaurant reviews as positive or negative, providing clear insights into customer opinions. Sentiment Analysis: Helps restaurants gain valuable insights into customer sentiments, allowing for better understanding of strengths and areas needing improvement. Business Improvement: Useful for restaurant owners, marketers, and data analysts to enhance customer satisfaction, improve services, and make data-driven decisions. Open Source: The entire project is available on GitHub, allowing you to explore the code, contribute, and adapt it to your specific needs. By leveraging this tool, businesses can gain a deeper understanding of customer feedback, tailor their services more effectively, and ultimately enhance the dining experience. Check out the project, explore the code, and see it in action by visiting the GitHub repository: https://lnkd.in/d_8vhJqq I look forward to your feedback and thoughts on this project. Your insights will be invaluable as I continue to refine and improve this tool. #MachineLearning #DeepLearning #AI #NLP #RestaurantReviews #DataScience #SentimentAnalysis #ArtificialIntelligence #GithubProjects #CustomerFeedback Looking forward to your feedback!

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