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.
We guarantee that all our online courses will meet or exceed your
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Course Curriculum
| Unit 01: Getting an Idea of NLP and its Applications | |||
| 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 | |||
| Unit 02: Feature Engineering | |||
| 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 | |||
| Unit 03: Dealing with corpus and WordNet | |||
| Module 01: Introduction | |||
| 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 | |||
| Unit 04: Create your Vocabulary for any NLP Model | |||
| 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 | |||
| Unit 05: Word2Vec in Detail and what is going on under the hood | |||
| Module 01: Introduction | |||
| 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 | |||
| Unit 06: Find and Represent the Meaning or Topic of Natural Language Text | |||
| 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 | |||