Natural Language Processing (NLP) has transformed how enterprises automate communication, analyze unstructured text, and develop intelligent conversational systems. This course provides an in-depth exploration of modern NLP techniques, from classical text processing to advanced transformer architectures and foundation models.
Learners will explore tokenization, word embeddings, sequence modeling, attention mechanisms, encoder-decoder architectures, BERT, GPT, T5, LLaMA, Retrieval-Augmented Generation (RAG), semantic search, and AI agent integration. Practical implementation is carried out using Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, FAISS, Pinecone, and ONNX Runtime.
The curriculum emphasizes production deployment, GPU optimization, distributed inference, MLOps, model observability, and enterprise-grade NLP system architecture.
Curriculum
- 9 Sections
- 0 Lessons
- 10 Weeks
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- Foundations of Natural Language ProcessingDescription Build a strong foundation in Natural Language Processing by understanding text representation, linguistic analysis, syntax, semantics, morphology, tokenization strategies, and enterprise NLP workflows. Lessons Introduction to NLP Computational Linguistics Text Processing Tokenization Lemmatization Stop Word Removal0
- Text Representation & Embedding ModelsDescription Understand modern embedding techniques including TF-IDF, Word2Vec, GloVe, FastText, contextual embeddings, and Sentence Transformers used for semantic understanding. Lessons Bag of Words TF-IDF Word2Vec GloVe FastText Sentence Embeddings0
- Transformer Architecture EngineeringDescription Study the internal architecture of Transformer networks including positional encoding, self-attention, multi-head attention, encoder-decoder blocks, masking strategies, and scaling laws. Lessons Transformer Fundamentals Self-Attention Multi-Head Attention Positional Encoding Encoder Architecture Decoder Architecture0
- Large Language Models (LLMs)Description Develop expertise in foundation models including GPT, BERT, RoBERTa, T5, LLaMA, and Mistral while understanding token prediction, instruction tuning, and inference optimization. Lessons GPT Architecture BERT RoBERTa T5 LLaMA Foundation Models0
- Prompt Engineering & LLM Fine-TuningDescription Implement prompt optimization, supervised fine-tuning, LoRA, QLoRA, PEFT, RLHF concepts, quantization techniques, and enterprise model customization workflows. Lessons Prompt Engineering Few-Shot Learning LoRA QLoRA PEFT Model Quantization0
- Retrieval-Augmented Generation (RAG)Description Build intelligent knowledge retrieval systems integrating vector databases, semantic embeddings, reranking algorithms, and enterprise document intelligence platforms. Lessons RAG Architecture FAISS Pinecone Qdrant LangChain Hybrid Search0
- Conversational AI & Intelligent ChatbotsDescription Develop enterprise conversational assistants using memory architectures, dialogue management, function calling, tool invocation, and AI workflow orchestration. Lessons Chatbot Architecture Memory Management Tool Calling Function Calling AI Agents Conversation Design0
- Enterprise Deployment & MLOpsDescription Deploy scalable NLP services using Docker, Kubernetes, FastAPI, MLflow, TensorRT, ONNX Runtime, monitoring platforms, and cloud-native AI infrastructure. Lessons FastAPI Docker Kubernetes MLflow ONNX Runtime TensorRT0
- Enterprise NLP ProjectsDescription Develop production-ready Natural Language Processing applications solving real-world business problems using state-of-the-art language models. Projects AI Customer Support Assistant Enterprise Knowledge Chatbot Semantic Search Engine Document Question Answering Resume Screening System AI Email Classification Contract Analysis Platform Multilingual Translation Engine0
Basic Python and introductory machine learning knowledge are recommended, but the course starts with NLP fundamentals before progressing to advanced topics.
You'll work with Hugging Face Transformers, PyTorch, TensorFlow, spaCy, NLTK, LangChain, LangGraph, FAISS, Pinecone, Qdrant, FastAPI, Docker, and Kubernetes.
Yes. The course covers LoRA, QLoRA, PEFT, prompt engineering, instruction tuning concepts, and enterprise customization workflows.
Yes. You'll build Retrieval-Augmented Generation pipelines using vector databases, semantic embeddings, hybrid search, and reranking techniques.
Features
- Enterprise LLM Fine-Tuning Labs
- 12 Hands-on NLP Projects
- Downloadable Source Code
Target audiences
- NLP Engineers
- Machine Learning Engineers
- Generative AI Developers
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