- Performing Practical Text Processing (k4)
Students can preprocess and manipulate text data, implementing methods for tokenization, normalization, and data cleaning to prepare it for analysis.
- Implementing Sentiment Analysis with Bag-of-Words Models (k4)
Students are able to build and evaluate sentiment analysis models using bag-of-words representations and machine learning techniques.
- Working with and Visualizing Word Embeddings (k4)
Students can generate and manipulate word embeddings, understanding how to use these vector representations to capture semantic relationships between words.
- Developing Document Classification Models with PyTorch (k4)
Students are capable of implementing document classification models using PyTorch, training and testing models to categorize text data into different classes effectively.
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Students have acquired hands-on knowledge of key NLP techniques, including text processing, sentiment analysis, word embedding applications, and document classification. They understand the practical aspects of implementing these methods using machine learning and neural network frameworks, particularly focusing on model training, evaluation, and optimization with PyTorch.
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