U&P AI – Natural Language Processing (NLP) with Python
21 Students
Last updated: January 24, 2024
Language: English
Flexible Schedule
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.
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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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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 | ||
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This course includes:
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Skill Level
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Duration
5 hours, 51 minutes -
Students
21 Students