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U&P AI - Natural Language Processing (NLP) with Python

U&P AI - Natural Language Processing (NLP) with Python

Overview

Uplift Your Career & Skill Up to Your Dream Job – Learning Simplified From Home!

Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special U&P AI – Natural Language Processing (NLP) with Python Course. You’ll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today’s workplaces.

The U&P AI – Natural Language Processing (NLP) with Python Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This U&P AI – Natural Language Processing (NLP) with Python Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field.

This U&P AI – Natural Language Processing (NLP) with Python Course, like every one of Study Hub’s courses, is meticulously developed and well researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly.

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Why buy this U&P AI – Natural Language Processing (NLP) with Python?

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Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
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Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the U&P AI – Natural Language Processing (NLP) with Python there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.

Who is this course for?

This U&P AI – Natural Language Processing (NLP) with Python course is ideal for

Students
Recent graduates
Job Seekers
Anyone interested in this topic
People already working in the relevant fields and want to polish their knowledge and skill.

Prerequisites

This U&P AI – Natural Language Processing (NLP) with Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This U&P AI – Natural Language Processing (NLP) with Python was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This U&P AI – Natural Language Processing (NLP) with Python is a great way for you to gain multiple skills from the comfort of your home.

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: Tokenization 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
Assignment
	Assignment – U&P AI – Natural Language Processing (NLP) with Python	00:00:00