hajajji hajer

A Technical Consultant in Fullstack Development with Django and Angular CLI is an expert responsible for guiding organizations through the development, deployment, and optimization of web applications. This role demands a deep understanding of both back-end and front-end technologies to build efficient, scalable, and user-friendly applications. A Technical Consultant not only provides technical expertise but also plays a crucial role in understanding the client's needs, designing solutions, and ensuring seamless communication between the user interface (UI) and the server-side logic.

As the CEO of CurioLearn, I lead a dynamic and innovative team dedicated to transforming education through cutting-edge training programs. At CurioLearn, we focus on delivering high-quality courses in Full Stack Development, Python, web technologies, and much more. Our mission is to equip students and professionals in Tunisia with the skills and knowledge they need to thrive in today’s digital economy. With a passion for education and a commitment to empowering learners, I oversee the strategic direction of CurioLearn, ensuring that our programs meet the highest standards and remain accessible to all. I also focus on building partnerships with international institutions to provide unique opportunities for our students. If you’re interested in learning more about CurioLearn or exploring collaborations, feel free to connect!

TASKS: ✅ Implemented a data pipeline that reduced processing time by 40%, enabling faster decision-making and improving report timeliness. Enhanced data accuracy by 25%, minimizing manual errors and ensuring reliable insights. ✅ Developing Natural Language Processing models using NLTK ✅ Developing a data product (web app) analyzing and extracting insights from such numerous and complex unstructured documents(invoice) ✅ Developed and deployed an application automating invoice PDF scan processing with OCR and deep learning, reducing manual processing time by 80% and cutting costs by 30%. Achieved 95% accuracy in extracting crucial invoice data, enhancing efficiency. Increased productivity by 40% through streamlined workflows, enabling focus on higher-value tasks.

Skills: Git · Docker · NestJS · Node.js · TypeORM · Architecture RESTful · Talend Open Studio · Angular · Scrum · Services web RESTful · Esprit d’équipe · Développement full-stack · TypeScript · PostgreSQL · Clarity

this prediction system to assess the success of your app before you start banging away at your keyboard to get that app on the app store. Our prediction system analyses the app description you provide and it provides you back with a detailed analysis of your app's potential selling quotient. With the help of this detailed report, the app developers can adapt their app with respect to the market trend, so as to increase the selling ability of the app. Methodology: -Prediction systems are rooted on logic-based deductions. In the ideal case, all the apps that are very similar should perform equally on the app store. This is the fundamental idea behind our prediction model. -As we include real world constraints such as age-group focus area, geographical focus area, app size, and so on, into the model, it gets closer to the actual market performance. To find the similar descriptions, we apply Natural Language Processing on the App Store dataset to find the Nearest Neighbours of the new description entered, using TFIDF of the descriptions in the dataset and their Cosine Similarities with respect to the new description. -Our system analyses the new description entered and determines the detailed analysis such as the potential app store rating, total number of installs, top similar apps, and so on. The NLP model is served through a Flask Server at the Back-end and the detailed report is rendered on the web interface with the help of ReactJS at the Front-end. -The nlp_engine folder contains the Natural Language Processing Model and the Flask Server program files, while the ReactFrontend folder contains the ReactJS Web Interface program files, that talk to the Flask Server.

Machine learning topic: Design a machine learning application that classifies potential customers for predicting the act of buying a product. anacoda, Pyhton

Notre équipe (Creative Team) a obtenu le quatrième prix parmi les 20 équipes participantes. https://www.facebook.com/creative.team.tunisia/