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

Are you ready to turn text into insight? Learn NLP with Python and build models that power everything from chatbots to search engines.

Step into the world of AI with one of the most in-demand skills in data science—Natural Language Processing. Master how machines understand human language and open doors to a high-paying, future-proof career.

Natural Language Processing (NLP) is at the core of AI-driven communication technologies. This course walks you through the principles and practical aspects of NLP using Python. From understanding raw text to developing your own NLP pipeline, this hands-on course helps you unlock data from language. Whether you’re exploring sentiment analysis, machine translation, or topic modelling, this course equips you with real-world applications and industry insights to get you project-ready.

What is Included:

• Hands-on NLP training covering Word2Vec, feature engineering and text analysis
• Practical Python coding exercises with real-world datasets
• Expert tutor support throughout your learning journey
• Industry-relevant curriculum designed by AI specialists
• Free Certificate upon completion

What Makes This Course a Smart Career Move?

The demand for professionals skilled in NLP has surged, with applications in finance, healthcare, marketing, and customer service. According to job market data, NLP-related roles in the UK are projected to grow steadily, with AI Specialists earning an average of £60k–£90k annually.

By learning NLP with Python—one of the most used programming languages in data science—you’re equipping yourself with skills that are not only in demand but also highly transferable across industries. This course positions you at the intersection of language and machine intelligence, making you a valuable asset in today’s data-driven job market.

Enrol now to build intelligent language models and join the future of AI.

Reed Courses Certificate of Completion

Digital certificate - Included

• Unit 01: Getting an Idea of NLP and its Applications 1:17:09

  • 1: Section 1_2 - Introduction 02:59
  • 2: 3 - By The End Of This Course 01:18
  • 3: 4 - Installation 03:30
  • 4: 5 - Tips 00:30
  • 5: 6 - U - Tokenizing Data 01:15
  • 6: 7 - P - Tokenization 02:21
  • 7: 8 - U - Stemming 01:56
  • 8: 9 - P - Stemming 04:50
  • 9: 10 - U - Lemmatization 01:47
  • 10: 11 - P - Lemmatization 03:06
  • 11: 12 - U - Chunks 01:45
  • 12: 13 - P - Chunks 05:04
  • 13: 14 - U - Bag of Words 04:15
  • 14: 15 - P - Bag Of Words 04:20
  • 15: 16 - U - Category Predictor 04:29
  • 16: 17 - P - Category Predictor 05:49
  • 17: 18 - U - Gender Identification 01:07
  • 18: 19 - P - Gender Identifier 07:38
  • 19: 20 - U - Sentement Analysis 02:21
  • 20: 21 - P - Sentement Analyzer 06:58
  • 21: 22 - U - Topic Modeling 02:45
  • 22: 23 - P - Topic Modeling 05:54
  • 23: 24 - Summary 01:12

• Unit 02: Feature Engineering 27:14

  • 24: Section 2_1 - Introduction 01:36
  • 25: 2 - One Hot Encoding 02:26
  • 26: 3 - Count Vectorizer 03:30
  • 27: 4 - N-grams 03:56
  • 28: 5 - Hash 01:35
  • 29: 6 - Word Embedding 10:40
  • 30: 7 - FastText 03:31

• Unit 03: Dealing with corpus and WordNet 47:27

  • 31: Corpuses_WordNet - 1 - Introduction 01:06
  • 32: Corpuses_WordNet - 2 - in-built 05:45
  • 33: Corpuses_WordNet - 3 - external corpora 07:30
  • 34: Corpuses_WordNet - 4 - corpuses_FD 07:19
  • 35: Corpuses_WordNet - 5 - FD 05:57
  • 36: Corpuses_WordNet - 6 - WordNet 05:44
  • 37: Corpuses_WordNet - 7 - WordNet H_H 07:22
  • 38: Corpuses_WordNet - 8 - WordNet - The Average 06:44

• Unit 04: Create your Vocabulary for any NLP Model 1:43:04

  • 39: 1 - Introduction and Challenges 08:11
  • 40: 2-0Tokenization for Building your Vocabulary 02:25
  • 41: 2-1Tokenization for Building your Vocabulary 03:02
  • 42: 2-2Tokenization for Building your Vocabulary 07:11
  • 43: 2-3Tokenization for Building your Vocabulary 11:40
  • 44: 2-4Tokenization for Building your Vocabulary 06:13
  • 45: 2-5Tokenization DotProduct 03:12
  • 46: 2-6Measuring similarity using dotproduct 02:50
  • 47: 2-7-reducing dim of vocabA token improvement 02:03
  • 48: 2-8 N-grams for your vocabulary 09:30
  • 49: 2-9 Normalizing for your vocabulary 09:36
  • 50: 2-10 Case normalization for your vocab 05:10
  • 51: 2-11 When to use stemming and lemmatization 03:36
  • 52: 3 - 0Sentiment Analysis in details 05:00
  • 53: 3 - 1Approaches for SA 02:41
  • 54: vocab-17 - rule based 05:13
  • 55: vocab-18 - naive baies 10:21
  • 56: vocab-19 04:22
  • 57: vocab-20 - summary 00:48

• Unit 05: Word2Vec in Detail and what is going on under the hood 1:08:53

  • 58: word2vec-1 04:14
  • 59: word2vec-2 13:59
  • 60: word2vec-3-4 08:22
  • 61: word2vec-5 10:30
  • 62: word2vec-6 16:19
  • 63: word2vec-7 10:26
  • 64: word2vec-8 03:03
  • 65: word2vec-9 02:00

• Unit 06: Find and Represent the Meaning or Topic of Natural Language Text 25:27

  • 66: 1-keywordsearchVSss 04:04
  • 67: 2-problems in tfidf 10:00
  • 68: 3- TF-IDF vectors to Topic Vectors under 11:23

This course is designed for learners who want to dive into the exciting world of Natural Language Processing using Python. It covers essential NLP concepts, tools, and methods, starting from the basics and advancing to powerful representation techniques such as Word2Vec and custom vocabulary creation. You will also explore how to interpret and find meaning in text—key to building intelligent, responsive AI systems.

You’ll engage in interactive lessons, practical exercises, and a final assignment designed to reinforce your learning. By the end of this course, you’ll have the skills to develop your own NLP models, a foundational understanding of how machines process human language, and a solid step toward a successful career in AI and data science.

Learning Objectives:

• Understand the foundations and applications of NLP
  
• Perform feature engineering on text data
  
• Handle and manipulate corpora using tools like WordNet
  
• Build custom vocabularies and use Word2Vec for semantic understanding
  
• Identify key topics and meanings from natural language text
  

Why should you take this course?

NLP is one of the fastest-growing fields in artificial intelligence. As businesses continue to automate communication and decision-making, the demand for NLP professionals is higher than ever. This course provides a practical, project-based learning path designed for real-world applications. Whether you're new to data science or looking to expand your skill set, this course gives you a competitive edge in a high-growth area.

Course Curriculum:

• Module 01: Getting an Idea of NLP and its Applications
• Module 02: Feature Engineering
• Module 03: Dealing with corpus and WordNet
• Module 04: Create your Vocabulary for any NLP Model
• Module 05: Word2Vec in Detail and what is going on under the hood
• Module 06: Find and Represent the Meaning or Topic of Natural Language Text

• Aspiring data scientists and AI professionals
  
• Python learners interested in NLP
  
• Software developers seeking to specialise in AI
  
• Researchers and analysts handling text data
  
• Anyone looking to enter the AI and language technology industry

No prior experience required. Just a stable internet connection, a compatible device, and the motivation to learn NLP with Python.

• NLP Engineer – 55k to 80k
  
• Machine Learning Engineer – 60k to 95k
  
• AI Specialist – 60k to 90k
  
• Data Scientist – 45k to 85k
  
• Text Analytics Consultant – 50k to 75k

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