Natural Language Processing with Deep Learning

2Month/20 Hour                                                   Price:125,000

                                                                                      120,000 

 

 

Natural Language Processing with Deep Learning

This course provides an in-depth exploration of Natural Language Processing (NLP) with a focus on using deep learning techniques to process and analyze human language. Through this course, participants will learn how to apply neural networks, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformers, to solve complex NLP tasks such as text classification, machine translation, sentiment analysis, and more.

Course Objectives:

Understand the core concepts of Natural Language Processing (NLP)
Implement deep learning models to solve NLP tasks such as text generation, machine translation, and sentiment analysis
Work with advanced architectures like Transformers and BERT for state-of-the-art NLP solutions
Gain hands-on experience with popular deep learning frameworks like TensorFlow or PyTorch for NLP tasks

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Course Content

Module 1: Introduction to Natural Language Processing

Overview of NLP:

Introduction to natural language processing and its importance
Key challenges in NLP: Ambiguity, variability, and context understanding

Fundamentals of NLP

Tokenization, stemming, lemmatization, and word embeddings
Statistical NLP: N-grams, TF-IDF, and language modeling

Hands-on Lab:
Build a simple text processing pipeline for tokenization and sentiment analysis using Python's NLTK or SpaCy library

Module 2: Word Embeddings and Representation 
Vector Representation of Words:

Word2Vec, GloVe, and FastText embeddings
Understanding the mathematics behind word embeddings
Applications of word embeddings in NLP tasks

Contextual Word Embeddings:

Introduction to ELMo and BERT
How contextual word embeddings improve performance in NLP tasks

Hands-on Lab:
Implement Word2Vec using Gensim to generate word embeddings and explore nearest neighbor

Module 3: Sequence Models for NLP – RNN, LSTM, and GRU 

Recurrent Neural Networks (RNNs):

Understanding RNNs and their role in sequential data
Limitations of RNNs: Vanishing and exploding gradients

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs):How LSTMs and GRUs solve the vanishing gradient problem
Applications of LSTM/GRU in NLP tasks such as text classification and sequence generation

Hands-on Lab:
Build and train an LSTM model for text generation using TensorFlow/Kera

Module 4: Attention Mechanism and Transformers 

The Attention Mechanism:

Introduction to attention and its significance in NLP tasks
How attention helps in aligning source and target in sequence-to-sequence models

Transformers:

Overview of Transformer architecture
Encoder-decoder model and self-attention mechanism
Understanding positional encoding

Applications of Transformers in NLP:
Machine translation, text summarization, and text generation

Hands-on Lab:
Implement a Transformer model using TensorFlow for machine translation

Module 5: Pretrained Language Models – BERT, GPT, and Beyond

Introduction to Pretrained Models:

What are pretrained language models and why they are powerful
Overview of BERT, GPT, and T5 architectures
Fine-tuning pretrained models for specific NLP tasks

Bidirectional Encoder Representations from Transformers (BERT):

Understanding the BERT architecture and its applications in NLP
How to use BERT for text classification, sentiment analysis, and question-answering

Generative Pretrained Transformer (GPT) Models:
Overview of GPT, GPT-2, and GPT-3 for text generation and conversational AI

Hands-on Lab:
Fine-tune a BERT model for sentiment classification using Hugging Face's Transformers library

Module 6: NLP Applications with Deep Learning 

Sentiment Analysis and Text Classification:

How to build and train models for sentiment analysis using LSTM and BERT
Text classification for spam detection, product categorization, etc

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Machine Translation and Text Summarization:

Use sequence-to-sequence models for translating text from one language to another

Abstractive and extractive text summarization

Question Answering Systems:
Building models for answering questions from text using pretrained models like BERT

Hands-on Lab:
Build a text summarization tool using an attention-based Transformer model

Module 7: Conversational AI and Chatbots 

Introduction to Conversational AI:

How NLP powers modern conversational agents
Dialogue management and intent recognition

Building a Chatbot with Deep Learning:

Integrating LSTM and Transformer models into chatbot systems
How to handle multi-turn dialogues and context management

Hands-on Lab:
Build a simple chatbot using GPT and deploy it on a messaging platform

Career Path After Completion:

Upon completion of this course, learners can explore various career opportunities in the field of NLP and AI, such as:

NLP Engineer: Develop models for text analysis, summarization, and translation
AI Researcher: Contribute to advancing state-of-the-art techniques in NLP and deep learning
Data Scientist: Apply NLP techniques to extract insights from unstructured text data
Chatbot Developer: Build and deploy conversational agents for customer service or virtual assistants
Machine Learning Engineer: Work on integrating deep learning solutions into NLP applications

International Student Fees; USD 525

Job Interview Preparation  (Soft Skills Questions & Answers)

Tough Open-Ended Job Interview Questions
What to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview

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Internships, Freelance and Full-Time Work opportunities

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Hire an Intern

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Flexible Class Options

Week End Classes For Professionals  SAT | SUN
Corporate Group Training Available
Online Classes – Live Virtual Class (L.V.C), Online Training

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