Natural Language Processing with Deep Learning

Natural Language Processing (NLP) is one of the most transformative fields of Artificial Intelligence, powering applications such as chatbots, sentiment analysis, language translation, and intelligent search. This 7-day corporate training program introduces participants to modern NLP techniques using Deep Learning frameworks like TensorFlow, PyTorch, and Hugging Face Transformers. Participants will gain a comprehensive understanding of language modeling, embedding methods, transformer architectures, and fine-tuning large language models (LLMs) such as BERT and GPT for enterprise-grade applications. The program focuses on both theory and practical implementations, enabling professionals to design, deploy, and optimize NLP solutions at scale.

Objectives of the Training

Understand the evolution and fundamentals of NLP and language models.
Learn modern NLP workflows using embeddings, RNNs, LSTMs, and Transformers.
Gain practical experience in implementing BERT, GPT, and LLM-based architectures.
Learn fine-tuning techniques for domain-specific text applications.
Explore ethical AI practices and challenges in NLP deployment.
Apply NLP models to real-world enterprise problems such as sentiment analysis, Q&A, and document summarization.

Prerequisites

Solid understanding of Python programming.
Familiarity with Machine Learning concepts and frameworks.
Basic knowledge of deep learning fundamentals.
Exposure to TensorFlow or PyTorch is recommended but not mandatory.

What You Will Learn

Fundamentals of NLP and text preprocessing.
Word embeddings: Word2Vec, GloVe, and contextual embeddings.
Transformer architecture and attention mechanisms.
Fine-tuning pre-trained models like BERT and GPT.
Building NLP pipelines for enterprise use cases.
Best practices for deployment and scalability of NLP models.

Target Audience

This training is designed for Data Scientists, AI Engineers, NLP Practitioners, and Research Analysts interested in mastering state-of-the-art NLP models and applications. It is also ideal for Technical Leads, Product Managers, and AI Strategists responsible for integrating NLP-driven intelligence into enterprise products and services.

Detailed 7-Day Curriculum

Day 1 – Introduction to NLP and Text Representation (6 Hours)

Session 1: Overview of NLP and its Evolution in the Enterprise Context.
Session 2: Text Preprocessing – Tokenization, Lemmatization, and Stopword Removal.
Session 3: Bag of Words, TF-IDF, and Word Embeddings.
Hands-on: Text Cleaning and Feature Extraction using NLTK and SpaCy.

Day 2 – Classical NLP and Introduction to Deep Learning (6 Hours)

Session 1: Statistical NLP Techniques – POS Tagging and Named Entity Recognition (NER).
Session 2: Introduction to Neural Networks for NLP – RNNs and LSTMs.
Session 3: Sequence-to-Sequence Models and Attention Mechanisms.
Case Study: Sentiment Analysis on Customer Reviews.

Day 3 – Transformers and Attention Mechanism (6 Hours)

Session 1: The Transformer Revolution – From Attention to Self-Attention.
Session 2: Architecture Breakdown – Encoder, Decoder, Multi-Head Attention.
Session 3: Implementing Transformer Models using PyTorch and TensorFlow.
Hands-on: Building a Custom Transformer for Text Classification.

Day 4 – BERT and Contextual Embeddings (6 Hours)

Session 1: Understanding Bidirectional Encoder Representations from Transformers (BERT).
Session 2: Tokenization, Masked Language Modeling, and Next Sentence Prediction.
Session 3: Fine-Tuning BERT for Named Entity Recognition and Sentiment Analysis.
Case Study: Enterprise Sentiment Analysis using BERT.

Day 5 – Large Language Models (LLMs) and GPT Architectures (6 Hours)

Session 1: Understanding GPT, T5, and BLOOM Architectures.
Session 2: Prompt Engineering and Fine-Tuning LLMs for Custom Applications.
Session 3: Retrieval-Augmented Generation (RAG) for Domain-Specific Knowledge.
Case Study: Automating Customer Support with GPT-based Chatbots.

Day 6 – NLP Pipelines, Evaluation, and Deployment (6 Hours)

Session 1: Evaluation Metrics – BLEU, ROUGE, and Perplexity.
Session 2: Deploying NLP Models using Hugging Face and Flask APIs.
Session 3: Integrating NLP Systems with Enterprise Workflows and Cloud Platforms.
Workshop: Building an NLP-Powered Document Search Engine.

Day 7 – Capstone Project & Future of NLP (6 Hours)

Session 1: Capstone Project Development – Choose a Real-World NLP Challenge.
Session 2: Model Training, Evaluation, and Presentation.
Session 3: Panel Discussion – Responsible NLP and AI Ethics.
Group Discussion: The Future of Large Language Models in the Enterprise.

Capstone Project

Participants will apply NLP and deep learning techniques to solve a real-world enterprise problem. Sample projects include sentiment analysis for brand monitoring, automated document summarization, or chatbot creation. The capstone emphasizes model design, fine-tuning, deployment, and interpretability in an enterprise environment.

Evaluation & Certification Framework

Daily lab participation and hands-on assignments (30%).
Engagement in discussions and case studies (20%).
Final capstone project completion and presentation (50%).

Upon successful completion, participants will be awarded a ‘Certified NLP and LLM Specialist’ certificate from Anika Technologies.

Future Trends in NLP and Language AI

NLP is evolving rapidly with breakthroughs in generative models, multimodal learning, and real-time conversational AI. The convergence of NLP with reinforcement learning and multimodal AI is enabling applications that combine vision, speech, and text understanding. As LLMs become integral to business operations, the future will focus on personalization, contextual intelligence, and efficient AI at scale.