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The tutorial covers the course structure focusing on various machine learning concepts. It starts with FastAI, covering image processing and transfer learning using ResNet. The course then transitions to transformers for text, discussing applications in sequence classification and sentiment analysis, along with named entity recognition and part-of-speech tagging. Students learn about fine-tuning text classification with custom data, referencing a Turkish dataset. The tutorial also explores word embeddings, particularly focusing on the original BERT paper and the foundational concepts of word vectors, including Word2Vec. The content is designed to guide learners through the progression of techniques in natural language processing.
