Name: U&P AI – Natural Language Processing (NLP) with Python
Price: 25 GBP

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Curriculum

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. Also, you can have your printed certificate delivered by post (shipping cost £3.99). 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.

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	00:10:00
	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