Course Summary

Course 2025 Advanced Machine Learning and Deep Learning Projects. This advanced machine learning and deep learning course covers the following topics: SBERT and BERT: These pre-trained models are used for natural language processing tasks such as sentence classification, entity name recognition, and question answering. Sentence embeddings and similarity measures: techniques for representing sentences as numeric vectors and methods for comparing similarity between sentences. Clustering: algorithms for grouping similar data, such as k-means and hierarchical clustering. Text summarization: Techniques for automatically generating a concise summary of a longer text. Question Answering: Techniques for automatically answering questions based on a given text. Image clustering: Algorithms for grouping similar images. Image Search: Techniques for searching images based on their content. Throughout the course, students will work on hands-on projects that help them apply the concepts learned to real-world problems. They will also have the opportunity to implement the latest state-of-the-art techniques in the field to solve various NLP and CV problems.