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.
Overview
Syllabus
- Course 1: Foundations of NLP Data Processing
- Course 2: Modern Tokenization Techniques for AI & LLMs
- Course 3: Optimized Data Preparation for Large-Scale LLMs
- Course 4: Chunking and Storing Text for Efficient LLM Processing
Courses
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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.
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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.
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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.
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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.
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