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U&P AI - Natural Language Processing with Python
Course Highlights Be efficient in Python programming & boost your skills with the U&P AI – Natural Language Processing with …
- Course Highlights
Be efficient in Python programming & boost your skills with the U&P AI – Natural Language Processing with Python course. This course has been specially designed to help learners gain a good command of U&P AI – Natural Language Processing with Python, providing them with a solid foundation of knowledge to become a qualified professional.
With this course, containing on-demand video lectures and downloadable resources and affordable premium-quality E-learning content, you can learn at your own pace. The U&P AI – Natural Language Processing with Python covers the NPL, WordNet, Word2Vec applications which will allow you to enhance your CV, impress potential employers, and stand out from the crowd. The course will equip you with everything you need to succeed.
If you are looking for up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the tech industry, this is the right place. Enrol in U&P AI – Natural Language Processing with Python course today and learn from the very best the industry has to offer!
- Who is This Course for
- Ambitious learners who have already worked in the programming sector
- Individuals who have the enthusiasm to obtain a new skill
Course Curriculum
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: 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 | ||
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