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📝 Natural Language Processing

Tokenization • Embeddings • Language Models • Transformers • LLMs • Text Analytics

Master the art of understanding language — from text preprocessing and classical NLP to transformers, large language models, and cutting-edge AI applications.

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NLP Concepts & Technologies

NLP Basics

What is NLP, applications, challenges, pipeline.

Text Preprocessing

Cleaning, normalization, tokenization, stemming.

Regular Expressions in NLP

Pattern matching, text extraction, cleaning.

N-Gram Language Models

Markov assumption, smoothing, perplexity.

Language Models

TF-IDF

Term frequency, inverse document frequency, features.

Bag of Words

Document representation, vocabulary, features.

Part-of-Speech Tagging

HMM, Viterbi, tagsets.

Named Entity Recognition

Entities, CRF, rule-based, deep learning.

Dependency Parsing

Syntax, dependencies, transition-based.

Word Embeddings

Distributed representations, dense vectors.

Word2Vec

CBOW, Skip-gram, negative sampling.

GloVe

Global vectors, co-occurrence matrix.

FastText

Subword embeddings, OOV handling.

BERT Embeddings

Contextualized, bidirectional, dynamic.

RNN for NLP

Sequences, LSTMs, GRUs, language modeling.

LSTM Networks

Long-term dependencies, memory gates.

CNN for NLP

Text classification, filters, pooling.

Attention Mechanism

Self-attention, cross-attention, scaling.

Transformer Architecture

Encoder/decoder, multi-head attention, FFN.

BERT (Bidirectional Encoder)

Pre-training, MLM, NSP, fine-tuning.

GPT (Generative Pre-trained)

Auto-regressive, prompting, generation.

T5 (Text-to-Text Transfer)

Unified framework, transfer learning.

LLaMA

Open-source LLM, scaling, efficiency.

Sentiment Analysis

Polarity, emotion, opinion mining.

Text Classification

Text Classification

SVM, Naive Bayes, deep learning.

Text Classification

Machine Translation

NMT, transformers, evaluation metrics.

Text Summarization

Extractive, abstractive, evaluation.

Question Answering

SQuAD, reading comprehension, extractive.

Conversational AI

Dialogue, chatbots, intent recognition.

LLM Fine-Tuning

Instruction tuning, RLHF, LoRA.

Prompt Engineering

Few-shot, chain-of-thought, prompting.

RAG (Retrieval-Augmented)

Knowledge retrieval, generation.

Multilingual NLP

Cross-lingual, mBERT, XLM-R.

NLP Evaluation Metrics

BLEU, ROUGE, METEOR, Perplexity.

NLP Benchmarks

GLUE, SuperGLUE, SQuAD.

NLTK

Python NLP library, tokenization, corpora.

spaCy

Industrial NLP, pipelines, models.

Hugging Face Transformers

Pre-trained models, tokenizers, pipelines.

Speech Recognition

ASR, acoustic models, Whisper.

Text-to-Speech

Synthesis, voices, neural TTS.

NLP Ethics

Bias, fairness, toxicity, responsible AI.

Bias Mitigation

Debiasing, counterfactuals, fairness.

📝 Natural Language Processing Mastery Roadmap

🔰 Foundation

Text preprocessing, regex, tokenization, NLTK

📶 Core

N-grams, POS tagging, NER, word embeddings, classification

🔥 Deep Dive

RNN/LSTM, Transformers, BERT, fine-tuning, translation

🏆 Frontier

LLMs, RAG, prompt engineering, multimodal NLP

📝 Understand Language — Master Natural Language Processing

Hands-on projects, real datasets, and cutting-edge NLP challenges.

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