INTRODUCTION Greetings once again! Today, I am thrilled to share with you an exciting project that has surpassed all my expectations– but hey, I say that every time, right? Throughout my time with Azubi, I have worked on various projects, each one seemingly better than the last. However, this particular project stands out as, introduced me to unstructured datasets, deep learning (specifically natural language processing), and the concept of transfer learning through fine-tuning a model. So, what sets this particular project apart, you might wonder? In our initial four projects, we delved into structured datasets and explored fundamental machine learning algorithms. However, what makes this endeavor truly extraordinary is its jump into unstructured datasets, deep learning (specifically, natural language processing), and the intricate realm of transfer learning – fine-tuning a model for specialized training on unique data. In this project, we had the opportunity to utilize the datas
Image by pikisuperstar on Freepik Introduction In the ever-evolving world of data science and technology, the task of translating data insights into interactive and accessible web applications has historically been a daunting challenge. Even with the most potent machine learning models and insightful data visualizations at your disposal, bridging the gap between technical know-how of programming languages and user-friendly web applications often demanded a steep learning curve in web development. However, a groundbreaking solution has surfaced, ready to reshape the entire landscape of data science and web application development. The era of viewing these fields as intricate enigmas demanding extensive study, thick textbooks, and years of practice is gradually waning and passing Meet Streamlit, an innovative open-source Python framework poised to transform the