Natural Language Processing
Enterprise Natural Language Processing: Transformer Architectures, LLM Fine-Tuning & Generative AI Engineering
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Teacher

Mahendran

Last Update:

January 19, 2022

Review:

Master enterprise Natural Language Processing (NLP) by building intelligent language understanding systems using Transformers, Large Language Models (LLMs), Hugging Face, BERT, GPT architectures, Retrieval-Augmented Generation (RAG), vector embeddings, and production-scale AI deployment.

Enterprise Natural Language Processing: Transformer Architectures, LLM Fine-Tuning & Generative AI Engineering
User Avatar

Enterprise Natural Language Processing: Transformer Architectures, LLM Fine-Tuning & Generative AI Engineering

10 Weeks
All levels
8 lessons
0 quizzes
0 students

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.

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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Enterprise Natural Language Processing: Transformer Architectures, LLM Fine-Tuning & Generative AI Engineering
$18.00
- 68% OFF
  • Instructor : Mahendran
  • Lectures : 8
  • Duration : 10 weeks
  • Enrolled : 0 students
  • Language: :English

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