Natural Language Processing

Master the fundamentals of Natural Language Processing (NLP) and learn how to process, analyze, and generate human language using Python. This course covers key NLP concepts, including tokenization, text preprocessing, sentiment analysis, named entity recognition (NER), machine learning models for NLP, and deep learning techniques like transformers.

Satyajit Pattnaik

Instructor

What you will learn?

Understand NLP Fundamentals
Perform Text Processing & Feature Engineering
Work with Large Language Models (LLMs)
Understand Ethical Considerations in NLP

This Course Includes

Recorded Lessons: 44
Recorded Hours: 9
Duration: 9 days (Avg)

TD Assessment Available
Access on Mobile

Course Description

The meaning of NLP is Natural Language Processing (NLP) which is a fascinating and rapidly evolving field that intersects computer science, artificial intelligence, and linguistics. NLP focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. With the increasing volume of text data generated every day, from social media posts to research articles, NLP has become an essential tool for extracting valuable insights and automating various tasks.

Course Content

44 Lessons | 8hr 53min

Frequently Asked Questions

This course is ideal for beginners to intermediate learners who are interested in understanding how machines can understand, interpret, and generate human language. It’s suitable for those with backgrounds in computer science, data science, and AI.

Introduction to NLP and its applications, Neural Networks, NLP Basics and many more.

The course will consist of a mix of lectures, quizzes. We will work on practical problems, allowing you to build real-world NLP applications.

The course is typically structured to be completed over a few weeks, with flexible learning hours. The exact duration depends on your pace.

Basic knowledge of machine learning concepts such as supervised and unsupervised learning can be helpful. However, the course will cover the necessary machine learning concepts as they relate to NLP.

(4.1)

8hr 53min

₹999

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Natural Language Processing

Satyajit Pattnaik

What you will learn?

This Course Includes

Course Description

Course Content

Introduction

NLP Introduction Part 1 6m

NLP Introduction Part 2 6m

NLP Key Challenges 9m

Linguistics 7m

NLP Basics

Case_Folding 4m

SCR (New) 14m

Handling Contractions (New) 10m

Tokenization 5m

Stop Words Removal 7m

nGrams 8m

Vectorization 4m

Word Embeddings 2m

BoW 9m

BoW Practicals 19m

TFIDF Practicals 21m

PoS & NER 12m

NER Practicals 12m

TFIDF 12m

Neural Network

Bi Directional RNN 27m

GRU Part 1 5m

GRU Part 2 25m

GRU Part 3 (resetGate) 8m

GRU Part 4 (updateGate) 9m

GRU Part 5 Practical's 12m

LSTM Part 1 19m

LSTM Part 2 (Architecture) 16m

LSTM Part 3 (Deep Dive Architecture) 6m

LSTM Part 4 (Pointwise Operations) 6m

LSTM Part 5 forgetGate 12m

LSTM Part 6 inputGate 18m

LSTM Part 7 outputGate 9m

LSTM Part 8 Practical 33m

LSTM Part 9 Practical 17m

LSTM Part 10 Practical 13m

Word Embeddings

WORD EMBEDDINGS START - Word2Vec Intro 6m

WORD EMBEDDINGS START -Word2Vec Part 2 3m

WORD EMBEDDINGS START -pretrained Word2Vec 7m

WORD EMBEDDINGS START -Word2Vec INTUITION 8m

WORD EMBEDDINGS START -Word2Vec 50Features 10m

WORD EMBEDDINGS START -Word2Vec CBOW 19m

WORD EMBEDDINGS START -Word2Vec SkipGrams 11m

WORD EMBEDDINGS START - GloVe 18m

WORD EMBEDDINGS START -fastText 11m

WORD EMBEDDINGS START -Cosine Similarity 18m

Frequently Asked Questions

Who should take this course?

What are the key topics covered in the NLP course?

How will the course be structured?

What is the expected duration of the course?

Do I need to have knowledge of machine learning for this course?

₹999