Grow Data Skills

Introduction NLP Foundations and Sequence Models

Introduction to NLP from Scratch
- Introduction to NLP:
  - Definition
  - Applications
  - Key challenges
- Basic Concepts:
  - Tokenization
  - Stemming
  - Lemmatization
  - Stopword Removal
- Feature Extraction:
  - Traditional methods: Bag of Words (BoW), TF-IDF
  - Introduction to embeddings: Word2Vec, GloVe
Sequence Models – RNN and LSTM
- Introduction to Sequence Models:
  - How NLP tasks handle sequential data
  - Overview of Recurrent Neural Networks (RNNs)
  - Challenges of RNNs: vanishing gradients
- LSTM and GRU:
  - Understanding Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
  - How LSTM solves the vanishing gradient problem
- Use Cases of Sequence Models:
  - Text Generation
  - Machine Translation

Project 1 – NewsIQ: Automated News Headline Generator

Advanced Theory – Text Summarization and Generation
- Text Summarization:
  - Techniques: extractive vs. abstractive summarization.
  - Introduction to modern models for summarization (T5, BART).
- Text Generation:
  - How RNNs and LSTMs generate text sequences.
  - Attention mechanism in sequence-to-sequence models.
Practical - Headline Generation and Summarization of News Articles
- Objective:
  - Build a system to generate headlines and summaries for news articles.
- Dataset:
  - News articles (e.g., from CNN, BBC).
- Tasks:
  - Preprocess the dataset and apply LSTM for text summarization.
  - Fine-tune a model like T5 or BART for headline generation.
  - Evaluate results using ROUGE and BLEU scores.

Project 2 – LinguaFlow: Multilingual Translator App

Advanced Theory – Machine Translation and Multilingual NLP
- Introduction to Machine Translation:
  - How sequence models are used in translation tasks.
  - Challenges in translating between multiple languages.
- Transformer Model for Translation:
  - Understanding transformer architecture and attention mechanisms.
  - Introduction to models like MarianMT, mBERT.
Practical - Building a Multilingual Machine Translation System
- Objective:
  - Build a translation system that supports multiple languages.
- Dataset:
  - Multilingual datasets (e.g., WMT datasets).
- Tasks:
  - Preprocess multilingual text data.
  - Fine-tune MarianMT for translating between languages (e.g., English, French, Hindi).
  - Evaluate translation quality using BLEU or METEOR.
- Deployment:
  - Deploy the translation model using FastAPI, create an API, and test with Postman.

Project 3 – ChatFusion: GenAI-Powered Conversational Agent

Advanced Theory – GenAI and Conversational AI
- Conversational AI Overview:
  - Use of Generative AI for building intelligent chatbots.
  - Understanding dialogue flow, intent recognition, and response generation.
- Large Language Models (LLMs):
  - GPT-3, GPT-4, and their applications in conversational agents.
  - Ethics and responsible use of Generative AI.
Practical - GenAI-Powered Chatbot
- Objective:
  - Build a chatbot capable of dynamic conversations on specific topics.
- Tasks:
  - Fine-tune a GPT model for conversational AI.
  - Integrate with dialogue flow and context handling for meaningful interactions.
- Deployment:
  - Deploy the chatbot using Docker and FastAPI.
  - Expose an API for the chatbot and create a basic web interface.

Course Starts On:

Live

7-Dec-2024

Course Duration:

16+ Hours

Session:

Validity:

1 Year (Starting From The Date Of Enrollment)

Class Timing:

Saturday & Sunday [4:00 PM - 6:00 PM Live Teaching, 6:00 PM to 6:45 PM Live Doubt Session] (IST)

Class Duration:

2 Hours Live Teaching, 45 minutes Doubt Solving

Class Recording Provided:

Yes

Programming Language Used:

Python

Prerequisite:

Python

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NLP Mastery With Industry Projects (Live Classes)

LanguageEnglish

Sessions 8

Duration 1 Month

Starts On 7-Dec-2024

Validity 1 Year

Mode Live

Tech stack you'll learn

Course Content

Course Schedule

Instructor

Shubhankit Sirvaiya

NLP Mastery With Industry Projects (Live Classes)

Namaste 🙏