- Note!
- Introduction to NLP
- By The End Of This Section
- Installation
- Tips
- U - Tokenization
- P - Tokenization
- U - Stemming
- P - Stemming
- U - Lemmatization
- P - Lemmatization
- U - Chunks
- P - Chunks
- U - Bag Of Words
- P - Bag Of Words
- U - Category Predictor
- P - Category Predictor
- U - Gender Identifier
- P - Gender Identifier
- U - Sentiment Analyzer
- P - Sentiment Analyzer
- U - Topic Modeling
- P - Topic Modeling
- Summary
Online
₹ 449 799
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
Fees information
₹ 449 ₹799
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Getting an Idea of NLP and its Applications
Feature Engineering
- Using Google Colab
- Introduction
- One Hot Encoding
- Count Vectorizer
- N-grams
- Hash Vectorizing
- Word Embedding
- FastText
Dealing with corpus and WordNet
- Introduction
- In-built corpora
- External Corpora
- Corpuses & Frequency Distribution
- Frequency Distribution
- WordNet
- Wordnet with Hyponyms and Hypernyms
- The Average according to WordNet
Create your Vocabulary for any NLP Model
- Putting the previous knowledge together
- Introduction and Challenges
- 1 - Building your Vocabulary
- 2 - Building your Vocabulary
- 3 - Building your Vocabulary
- 4 - Building your Vocabulary
- 5 - Building your Vocabulary
- Dot Product
- Similarity using Dot Product
- Reducing Dimensions of your Vocabulary using token improvement
- Reducing Dimensions of your Vocabulary using n-grams
- Reducing Dimensions of your Vocabulary using normalizing
- Reducing Dimensions of your Vocabulary using case normalization
- When to use stemming and lemmatization?
- Sentiment Analysis Overview
- Two approaches for sentiment analysis
- Sentiment Analysis using rule-based
- Sentiment Analysis using machine learning - 1
- Sentiment Analysis using machine learning - 2
- Summary
Word2Vec in Detail and what is going on under the hood
- Introduction
- Bag of words in detail
- Vectorizing
- Vectorizing and Cosine Similarity
- Topic modeling in Detail
- Make your Vectors will more reflect the Meaning, or Topic, of the Document
- Sklearn in a short way
- Summary
Find and Represent the Meaning or Topic of Natural Language Text
- Note!
- Keyword Search VS Semantic Search
- Problems in TI-IDF leads to Semantic Search
- Transform TF-IDF Vectors to Topic Vectors under the hood