Learn to clean, tokenize, vectorize, and chunk text data for LLMs. Master modern tokenization, scalable data prep, deduplication, filtering, augmentation, and efficient storage for high-quality NLP pipelines.
Master the foundations of NLP data processing with hands-on practice in text cleaning, vectorization (TF-IDF, bag-of-words, embeddings), modern tokenization methods (BPE, WordPiece, SentencePiece), and efficient large-scale data prep for LLMs. You'll build pipelines that scale from basic preprocessing to embedding storage in vector databases.
Chunking and Storing Text for Efficient LLM Processing
4 lessons
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14 practices
This course covers tokenization techniques used in modern AI models, including rule-based methods, subword tokenization (BPE, WordPiece, SentencePiece), and vocabulary optimizations. Learners will implement these methods and understand their impact on NLP model performance.
This course teaches efficient data preparation strategies for training large-scale LLMs. It covers scalable data collection, deduplication, filtering, and augmentation techniques to ensure high-quality, diverse, and optimized datasets.
This course teaches learners how to chunk large text efficiently and store it in a database for structured retrieval. These techniques are essential for processing long documents in LLM applications such as search, retrieval, and knowledge management.
