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

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

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

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

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1 Year Access

Intermediate

3 Students

5 hours, 54 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow U&P AI – Natural Language Processing (NLP) with Python will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirement

- This course is available to all learners of all academic backgrounds.
- Learners should be aged 16 or over to undertake the qualification.
- Some basic understanding of the English language and numeracy.

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

- PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Why choose us?

- - Affordable, engaging & high-quality e-learning study materials;
  - Tutorial videos/materials from the industry-leading experts;
  - Study in a user-friendly, advanced online learning platform;
  - Efficient exam systems for the assessment and instant result;
  - The UK & internationally recognised accredited qualification;
  - Access to course content on mobile, tablet or desktop from anywhere, anytime;
  - The benefit of career advancement opportunities;
  - 24/7 student support via email.

Career path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry.

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	00:14:00
	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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U&P AI - Natural Language Processing (NLP) with Python