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Course Overview

Welcome to the new world of natural language processing and become a world-class practitioner of NLP with python. Our U&P AI - Natural Language Processing (NLP) with Python course can be the best option to swift up your career.

You will gain a good command of U&P AI - Natural Language Processing with Python, providing them with a solid foundation of knowledge to become a qualified professional and understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrases matching, tokenization, and more!

Enroll now and dive into this exciting world!

Learning Outcomes

Who is this course for?

By Completing This Course, You Can Excel in These in-demand Professions:

“Storytelling is the most powerful way to put ideas into the world today.”

— Robert McKee

Curriculum

Unit 01: Getting an Idea of NLP and its Applications
Module 01: Introduction to NLP 00:03:00
Module 02: By the End of This Section 00:01:00
Module 03: Installation 00:04:00
Module 04: Tips 00:01:00
Module 05: U – Tokenization 00:01:00
Module 06: P – Tokenization 00:02:00
Module 07: U – Stemming 00:02:00
Module 08: P – Stemming 00:05:00
Module 09: U – Lemmatization 00:02:00
Module 10: P – Lemmatization 00:03:00
Module 11: U – Chunks 00:02:00
Module 12: P – Chunks 00:05:00
Module 13: U – Bag of Words 00:04:00
Module 14: P – Bag of Words 00:04:00
Module 15: U – Category Predictor 00:05:00
Module 16: P – Category Predictor 00:06:00
Module 17: U – Gender Identifier 00:01:00
Module 18: P – Gender Identifier 00:08:00
Module 19: U – Sentiment Analyzer 00:02:00
Module 20: P – Sentiment Analyzer 00:07:00
Module 21: U – Topic Modeling 00:03:00
Module 22: P – Topic Modeling 00:06:00
Module 23: Summary 00:01:00
Unit 02: Feature Engineering
Module 01: Introduction 00:02:00
Module 02: One Hot Encoding 00:02:00
Module 03: Count Vectorizer 00:04:00
Module 04: N-grams 00:04:00
Module 05: Hash Vectorizing 00:02:00
Module 06: Word Embedding 00:11:00
Module 07: FastText 00:04:00
Unit 03: Dealing with corpus and WordNet
Module 01: Introduction 00:01:00
Module 02: In-built corpora 00:06:00
Module 03: External Corpora 00:08:00
Module 04: Corpuses & Frequency Distribution 00:07:00
Module 05: Frequency Distribution 00:06:00
Module 06: WordNet 00:06:00
Module 07: Wordnet with Hyponyms and Hypernyms 00:07:00
Module 08: The Average according to WordNet 00:07:00
Unit 04: Create your Vocabulary for any NLP Model
Module 01: Introduction and Challenges 00:08:00
Module 02: Building your Vocabulary Part-01 00:02:00
Module 03: Building your Vocabulary Part-02 00:03:00
Module 04: Building your Vocabulary Part-03 00:07:00
Module 05: Building your Vocabulary Part-04 00:12:00
Module 06: Building your Vocabulary Part-05 00:06:00
Module 07: Dot Product 00:03:00
Module 09: Reducing Dimensions of your Vocabulary using token improvement 00:02:00
Module 10: Reducing Dimensions of your Vocabulary using n-grams 00:10:00
Module 11: Reducing Dimensions of your Vocabulary using normalizing 00:10:00
Module 12: Reducing Dimensions of your Vocabulary using case normalization 00:05:00
Module 13: When to use stemming and lemmatization? 00:04:00
Module 15: Two approaches for sentiment analysis 00:03:00
Module 16: Sentiment Analysis using rule-based 00:05:00
Module 17: Sentiment Analysis using machine learning – 1 00:10:00
Module 18: Sentiment Analysis using machine learning – 2 00:04:00
Module 19: Summary 00:01:00
Unit 05: Word2Vec in Detail and what is going on under the hood
Module 01: Introduction 00:04:00