NLP Bootcamp 2026 From Zero to Production NLP Engineer

Master Natural Language Processing with Python, spaCy, Hugging Face Transformers, Large Language Models, RAG and more...
What you'll learn

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The complete NLP pipeline - from raw text to deployed API.
Text processing fundamentals: tokenisation, text cleaning, stemming, lemmatisation, regular expressions, fuzzy matching.
Interview preparation with top theory questions, coding challenges, and a mock interview.
Core NLP libraries: NLTK, spaCy, TextBlob, Gensim, Hugging Face transformers, datasets, tokenizers, Scikit‑learn.
Classical NLP models: Naive Bayes, Logistic Regression, SVMs, HMMs, CRFs, LDA, NMF.
Deep learning for NLP: RNNs, LSTMs, GRUs, Seq2Seq, Attention (Bahdanau & Luong), Transformer architecture.
Pre‑trained Transformers: BERT, RoBERTa, DistilBERT, GPT‑family models, fine‑tuning on custom tasks.
Key NLP tasks: sentiment analysis, NER, POS tagging, dependency parsing, machine translation, summarisation, question answering
Advanced topics: information retrieval, knowledge graphs, multilingual NLP, responsible AI (bias, fairness, explainability).
20+ real projects including spam detection, custom NER, news summariser, semantic search engine, chatbots, translation service, and document classification.
Requirements

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Basic Python programming (variables, loops, functions, classes).
Description

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Master Natural Language Processing in 2026 and become a production‑ready NLP engineer. This comprehensive NLP Bootcamp takes you from the fundamentals of text processing to the cutting edge of Large Language Models, Retrieval‑Augmented Generation (RAG), and multimodal AI.
You will learnPython, NLTK, spaCy, Hugging Face Transformers, PyTorch, TensorFlow, Gensim, Scikit‑learn, FAISS, LangChain, LoRA, QLoRA, MLflow, Docker, and more. The course covers the complete NLP pipeline: tokenisation, text cleaning, stemming, lemmatisation, regular expressions, fuzzy matching, TF‑IDF, Word2Vec, GloVe, FastText, contextual embeddings (ELMo, BERT), document embeddings, Byte‑Pair Encoding, SentencePiece, vector databases, and semantic search. You will build classical machine learning models (Naive Bayes, Logistic Regression, SVMs, HMMs, CRFs, LDA, NMF) and modern deep learning architectures (RNNs, LSTMs, GRUs, Seq2Seq, Attention, Transformer, BERT, GPT, RoBERTa, DistilBERT).
With20+ hands‑on projects, you will build spam detectors, sentiment analysers, custom NER models, news summarisers, question answering systems, chatbots, document classification systems, multilingual translation services, and a semantic search engine. You will fine‑tune LLMs usingLoRA and QLoRA, deployRAG pipelines with LangChain and LlamaIndex, and implement production‑gradeMLOps practices including experiment tracking, CI/CD, monitoring, and drift detection. You will also explore advanced topics such asreinforcement learning from human feedback (RLHF),agentic AI with tool use,speech recognition (Whisper),text‑to‑speech, andmultimodal NLP with vision‑language models.
The course includes dedicated sections oninterview preparation with top NLP theory questions, coding challenges, and a full mock interview. You will complete acapstone project to showcase in your portfolio and receive acareer roadmap with resume and portfolio guidance.
By the end of this NLP Bootcamp, you will be able tobuild, deploy, and scale NLP systems for real businesses - from classical machine learning models to state‑of‑the‑art Transformers and LLMs. You will confidently handletext classification, named entity recognition, machine translation, summarisation, question answering, and conversational AI. Whether you are a Python developer, data scientist, machine learning engineer, student, or entrepreneur, this course equips you with the skills to land a top NLP role in 2026 and beyond.
Key topics covered: Text processing, embeddings, Transformers, LLMs, RAG, fine‑tuning, deployment, MLOps, and responsible AI.
Enroll now and start your journey to becoming a world‑class NLP engineer.
Who this course is for

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Python developers who want to break into NLP and AI.
Data scientists and analysts who need to add text processing and language AI to their skillset.
Machine learning engineers who want production‑grade NLP, LLM fine‑tuning, and RAG expertise
Students and researchers who need practical, project‑based learning and an interview‑ready portfolio.
Entrepreneurs and builders who want to create chatbots, search engines, or language‑powered products.
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