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NLP Mastery With Industry Projects

By Shubhankit In Grow Data Skills

Self Paced
English
Completion Certificate

1 Year

Validity (From the date of Enrollment)

20 Hours

Duration

8

Sessions

3

Projects

INR 2000

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This course includes

Content Duration20 Hours
Total Video Session8
Total Industry Project3
Tools & Technology – Python, Jupyter Notebook, VS Code, NLTK, SpaCy, T5, BART, RNN, LSTM, GRU, Transformer Models, MarianMT, GPT Models (e.g., GPT-3, GPT-4), FastAPI, Postman, Docker , Azure
Certificate of completion

Course Content

✅ 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

✅ 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.

✅ 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.

✅ 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 Schedule

Mode of the Course:

Recorded

Course Duration:

20 Hours

Total Sessions:

Total Projects:

Validity:

1 Year (Starting From The Date Of Enrollment)

Programming Language Used:

Python

Prerequisite:

Basic Python

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Shubhankit Sirvaiya

AI Engineer @ Fidelity International Ex - World Wide Technology (WWT) & Accenture

Shubhankit Sirvaiya is an AI Engineer at Fidelity International, with prior experience as an AI Engineer at World Wide Technology (WWT) and as a Data Scientist at Accenture Growth & Strategy. He has 5+ years of hands-on experience across Data Science, Machine Learning, MLOps, and AI systems, working on real-world, production-grade problems. Alongside industry work, he has been teaching and mentoring for the last 4+ years, helping 1000+ students and professionals transition into Data Science and AI roles. He also runs an active LinkedIn community of 58k+ followers, where he shares practical insights around AI engineering, careers, and real industry expectations. His teaching style focuses on story-based learning and connecting the dots — breaking down complex AI concepts into clear mental models so learners don’t feel lost in the vast and often overwhelming AI syllabus. His goal is not just to teach tools, but to help learners understand why things work and how they fit together in real systems.