Be efficient in Python programming & boost your skills with the U&P AI – Natural Language Processing with Pythoncourse.This course has been specially designed to help learners 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.
With this course, containing on-demand video lectures and downloadable resources and affordable premium-quality E-learning content, you can learn at your own pace. The U&P AI – Natural Language Processing with Python covers the NPL, WordNet, Word2Vec applications which will allow you to enhance your CV, impress potential employers, and stand out from the crowd. The course will equip you with everything you need to succeed.
If you are looking for up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the tech industry, this is the right place. Enrol in U&P AI – Natural Language Processing with Python course today and learn from the very best the industry has to offer!
Learning outcome
Gain ideas about NLP and its applications
Familiar yourself with Feature Engineering
Learn about Corpuses & Frequency Distribution
Understand the WordNet and Word2Vec
Learn how to create vocabulary for any NLP Model
Explore and represent the Topic of Natural Language Text
Requirements
No formal qualifications required, anyone from any academic background can take this course.
Access to any internet-enabled smart device.
Why should I take this course?
6+ hours of on-demand video lectures and downloadable resources.
Affordable premium-quality E-learning content, you can learn at your own pace.
You will receive a completion certificate upon completing the course.
Internationally recognized Accredited Qualification will boost up your resume.
You will learn the researched and proven approach adopted by successful salespeople to transform their careers.
You will be able to incorporate various practical sales techniques successfully and understand your customers better.
Who is This Course for
Ambitious learners who have already worked in the programming sector
Individuals who have the enthusiasm to obtain a new skill
Course Curriculum
Course 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 08: Similarity using 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 14: Sentiment Analysis Overview
00:05:00
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
Module 02: Bag of words in detail
00:14:00
Module 03: Vectorizing
00:08:00
Module 04: Vectorizing and Cosine Similarity
00:11:00
Module 05: Topic modeling in Detail
00:16:00
Module 06: Make your Vectors will more reflect the Meaning, or Topic, of the Document
00:10:00
Module 07: Sklearn in a short way
00:03:00
Module 08: Summary
00:02:00
Unit 06: Find and Represent the Meaning or Topic of Natural Language Text
Module 01: Keyword Search VS Semantic Search
00:04:00
Module 02: Problems in TI-IDF leads to Semantic Search
00:10:00
Module 03: Transform TF-IDF Vectors to Topic Vectors under the hood
00:11:00
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