U&P AI - Natural Language Processing (NLP) with Python
4 enrolled
5h 50m of learning
Updated Nov 2026
CertificateOn completion
5h 50mSelf-paced
66 lessonsOn the UISC learning portal
All LevelsNo prior training needed
Course curriculum
1 modules66 lessons5h 50m total
01U&P AI - Natural Language Processing (NLP) with Python66 lessons
Module 01: Introduction to NLP
Module 02: By the End of This Section
Module 03: Installation
Module 04: Tips
Module 05: U – Tokenization
Module 06: P – Tokenization
Module 07: U – Stemming
Module 08: P – Stemming
Module 09: U – Lemmatization
Module 10: P – Lemmatization
Module 11: U – Chunks
Module 12: P – Chunks
Module 13: U – Bag of Words
Module 14: P – Bag of Words
Module 15: U – Category Predictor
Module 16: P – Category Predictor
Module 17: U – Gender Identifier
Module 18: P – Gender Identifier
Module 19: U – Sentiment Analyzer
Module 20: P – Sentiment Analyzer
Module 21: U – Topic Modeling
Module 22: P – Topic Modeling
Module 23: Summary
Module 01: Introduction
Module 02: One Hot Encoding
Module 03: Count Vectorizer
Module 04: N-grams
Module 05: Hash Vectorizing
Module 06: Word Embedding
Module 07: FastText
Module 02: In-built corpora
Module 03: External Corpora
Module 04: Corpuses & Frequency Distribution
Module 05: Frequency Distribution
Module 06: WordNet
Module 07: Wordnet with Hyponyms and Hypernyms
Module 08: The Average according to WordNet
Module 01: Introduction and Challenges
Module 02: Building your Vocabulary Part-01
Module 03: Building your Vocabulary Part-02
Module 04: Building your Vocabulary Part-03
Module 05: Building your Vocabulary Part-04
Module 06: Building your Vocabulary Part-05
Module 07: Tokenization Dot Product
Module 08: Similarity using Dot Product
Module 09: Reducing Dimensions of your Vocabulary using token improvement
Module 10: Reducing Dimensions of your Vocabulary using n-grams
Module 11: Reducing Dimensions of your Vocabulary using normalizing
Module 12: Reducing Dimensions of your Vocabulary using case normalization
Module 13: When to use stemming and lemmatization?
Module 14: Sentiment Analysis Overview
Module 15: Two approaches for sentiment analysis
Module 16: Sentiment Analysis using rule-based
Module 17: Sentiment Analysis using machine learning – 1
Module 18: Sentiment Analysis using machine learning – 2
Module 19: Summary
Module 02: Bag of words in detail
Module 03: Vectorizing
Module 04: Vectorizing and Cosine Similarity
Module 05: Topic modeling in Detail
Module 06: Make your Vectors will more reflect the Meaning, or Topic, of the Document
Module 07: Sklearn in a short way
Module 08: Summary
Module 01: Keyword Search VS Semantic Search
Module 02: Problems in TI-IDF leads to Semantic Search
Module 03: Transform TF-IDF Vectors to Topic Vectors under the hood
About this course
Overview:
Welcome to "U&P AI Natural Language Processing"! This course offers a comprehensive exploration of Natural Language Processing (NLP) techniques using artificial intelligence. NLP is a field of AI that focuses on enabling computers to understand, interpret, and generate human language. In this course, you'll delve into the principles and applications of NLP, learning how to build NLP models for tasks such as text classification, sentiment analysis, and language translation.
Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases
Main Course Features:
Thorough coverage of NLP fundamentals, including tokenization, stemming, and part-of-speech tagging
Hands-on projects and exercises for practical application of NLP techniques
Introduction to popular NLP libraries and frameworks such as NLTK, SpaCy, and Transformers
Exploration of advanced NLP tasks such as named entity recognition and text summarization
Real-world case studies and examples showcasing NLP applications in various domains
Access to datasets and resources for experimenting with NLP models
Supportive online community for collaboration and assistance throughout the course
Regular updates to keep pace with the latest advancements in NLP and AI technologies
Who Should Take This Course:
Data scientists and AI enthusiasts interested in delving into the field of Natural Language Processing
Software engineers and developers looking to incorporate NLP capabilities into their applications
Students and professionals seeking to enhance their skills in AI and machine learning with a focus on NLP
Linguists and language enthusiasts curious about the intersection of AI and human language processing
Learning Outcomes:
Understand the core concepts and techniques of Natural Language Processing
Build and train NLP models for various tasks, including text classification and sentiment analysis
Implement advanced NLP techniques such as named entity recognition and text summarization
Explore popular NLP libraries and frameworks for developing NLP applications
Apply NLP models to real-world datasets and analyze their performance
Develop a portfolio of NLP projects showcasing proficiency in NLP techniques and tools
Stay updated with the latest advancements and trends in Natural Language Processing and AI
Contribute to the advancement of NLP research and applications through continued learning and experimentation.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Assessment
At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.
Your certificate
CPD certificate — download it when you finish
Issued the moment you complete the final module. Every certificate lists the course aims and objectives, anticipated learning outcomes, the quality-assurance process — the elements required for verifiable CPD. Non-registrants can use it as CQC training evidence and for appraisals.
Aims & objectives
Learning outcomes
Unique certificate number
Common questions
Where do I take the course?
On the UISC learning portal. "Enrol now" takes you to this course there, where you pay securely, get your login and study at your own pace. Your certificate is issued there when you finish.
When can I start?
Immediately. Access opens the moment your order is confirmed and you can learn from any device, in any order, as many times as you like.
How is the course assessed?
A short multiple-choice or scenario-based assessment at the end of the course with instant results. You can retake it as many times as you need at no extra cost.
Can I enrol my whole team?
Yes. Organisations can buy multiple places on one invoice and receive a training record for every learner. Contact us for team pricing.
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