Table of Contents

Mentor Based

Natural Language Processing (NLP)

Unlock the Power of Language with Advanced NLP Techniques.

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Mentor Based

LEVEL
Moderate

DURATION
4 Weeks

About

This Program is designed to provide a comprehensive understanding of Natural Language Processing (NLP) techniques and applications. Participants will explore the foundational principles of NLP, including text preprocessing, tokenization, and sentiment analysis. The course will delve into advanced topics such as topic modeling, sequence models, and state-of-the-art transformer models like BERT. By the end of the course, participants will be proficient in using key NLP libraries and frameworks, preparing them for advanced studies or careers in NLP and AI.

Program Objectives

Understand and apply foundational NLP concepts and techniques.
Perform text preprocessing, cleaning, and normalization.
Implement tokenization, stemming, and lemmatization methods.
Build and evaluate sentiment analysis models.
Explore and apply topic modeling techniques such as LDA.
Develop sequence models using RNNs and LSTM networks.
Utilize transformer models like BERT for advanced NLP tasks.
Gain hands-on experience with NLP libraries including NLTK, spaCy, and Hugging Face Transformers.
Complete real-world NLP projects to demonstrate practical skills.
Prepare for advanced roles in NLP and AI through comprehensive training and hands-on practice.

Program Structure

Introduction to Natural Language Processing:

Overview of NLP and its Applications.
Key Concepts and Terminologies in NLP.
Text Preprocessing and Cleaning.

Text Processing and Tokenization:

Techniques for Text Normalization.
Tokenization Methods.
Stemming and Lemmatization.

Sentiment Analysis:

Basics of Sentiment Analysis.
Building Sentiment Analysis Models.
Evaluating Sentiment Analysis Models.

Topic Modeling:

Introduction to Topic Modeling.
Latent Dirichlet Allocation (LDA).
Implementation of Topic Modeling in Python.

Sequence Models:

Understanding Sequence Data.
Recurrent Neural Networks (RNN).
Long Short-Term Memory (LSTM) Networks.

Transformer Models:

Introduction to Transformer Models.
Bidirectional Encoder Representations from Transformers (BERT).
Implementing Transformers for NLP Tasks.

Practical NLP:

Working with Python and Jupyter Notebooks.
Using NLTK and spaCy for NLP Tasks.
Implementing Advanced NLP Models with Hugging Face Transformers.

Intended For

Senior undergraduates and graduate students in Computer Science and related fields.
Professionals in IT, data science, and software development looking to enhance their NLP skills.

Program Outcomes

Develop a solid understanding of NLP principles and techniques.
Gain proficiency in text preprocessing, tokenization, and sentiment analysis.
Implement topic modeling, sequence models, and transformer models like BERT.
Master the use of key NLP libraries such as NLTK, spaCy, and Hugging Face Transformers.
Apply NLP concepts to real-world projects and scenarios.
Enhance Python programming skills for advanced NLP tasks.
Complete practical coding exercises and projects demonstrating NLP expertise.
Earn a certificate of completion recognized by industry leaders.

Mentors

AI Mentor

Rajnish Tandon

Bodhi Nexus (Founder)

Biography

AI Mentor

Pratish Jain

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Biography

AI Mentor

J. T. Sibychen

Cyber and Cloud Security Trainer

NIIT Foundation

Biography

More Mentors

Fee Structure

Fee: INR 10,999 USD 164

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

List of Currencies

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Key Takeaways

Access to e-LMS
Real Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Future Career Prospects

NLP Engineer: Design and implement natural language processing models and applications.
Data Scientist: Analyze text data to extract meaningful insights and drive business decisions.
AI Research Scientist: Conduct research in NLP and contribute to advancements in the field.
Machine Learning Engineer: Develop and optimize machine learning models for text analysis.
AI Product Manager: Oversee the development and deployment of NLP-based products.
Computational Linguist: Work on the intersection of computer science and linguistics to improve language technologies.