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

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

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Overview

The U&P AI – Natural Language Processing (NLP) with Python is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an accredited certificate after completing the course. Enrol now for a limited-time discounted price.

Like all the courses of One Education, this U&P AI – Natural Language Processing (NLP) with Python is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.

We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support. 

Our expert instructors are always available to answer all your questions and make your learning experience exquisite.

After completing the U&P AI – Natural Language Processing (NLP) with Python, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.

If you want to learn about this topic and achieve certifications, you should consider this U&P AI – Natural Language Processing (NLP) with Python from One Education.

There are no hidden fees or exam charges. We are very upfront and clear about all the costs of the course. 

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised certification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.

Why study this course

Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.

The U&P AI – Natural Language Processing (NLP) with Python is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

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 00:10:00
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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