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

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

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8 STUDENTS

Learning Outcome

Learn in-depth about every detail and build real projects in NLP.
Convert text to numerical values for machine learning models and feature engineering.
Be able to convert words to their base forms using stemming.
Know how to extract document term matrix using the bag of words model.
Tokenize text data.

Description

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 Thames College, 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 Thames College; we provide a rich learning experience. After purchasing a course from Thames College, 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 Thames College.

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