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From tokenization to transformers β master NLP with code, math, and production-grade implementations.
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What You Will Learn
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Tokenization & Embeddings
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Text Classification
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NER & Sequence Labeling
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Transformers & Attention
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BERT & GPT Models
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Sentiment Analysis
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Semantic Search & RAG
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Multilingual NLP
All Lessons (50)
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- β’What Is Nlp
- β’Text Preprocessing
- β’Tokenization
- β’Stemming Lemmatization
- β’Bag Of Words
- β’Tf Idf
- β’N Grams
- β’Text Similarity
- β’Sentiment Analysis
- β’Text Classification
- β’Named Entity Recognition
- β’Part Of Speech Tagging
- β’Word Embeddings
- β’Word2vec
- β’Rnn For Nlp
- β’Lstm Gru
- β’Sequence To Sequence
- β’Attention Mechanism
- β’Transformer Architecture
- β’Bert Model
- β’Gpt Models
- β’Tokenization Deep Dive
- β’Fine Tuning Transformers
- β’Text Generation
- β’Question Answering
- β’Summarization
- β’Machine Translation
- β’Text Summarization
- β’Dialogue Systems
- β’Language Modeling
- β’Semantic Search
- β’Retrieval Augmented Generation
- β’Named Entity Linking
- β’Relation Extraction
- β’Sentiment Analysis Deep Learning
- β’Text Generation Evaluation
- β’Prompt Engineering
- β’Instruction Tuning
- β’Rlhf Alignment
- β’Hallucination Detection
- β’Nlp Evaluation Metrics
- β’Nlp Datasets Benchmarks
- β’Nlp Data Augmentation
- β’Deploying Nlp Models
- β’Nlp Monitoring Observability
- β’Multilingual Nlp
- β’Nlp Evaluation Advanced
- β’Nlp Transfer Learning
- β’Nlp Few Shot Learning
- β’Nlp Future Trends
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