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

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                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Getting an Idea of NLP and its Applications

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

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

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

Beginner

Online

₹ 1499

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The syllabus

Getting an Idea of NLP and its Applications

Feature Engineering

Dealing with corpus and WordNet

Create your Vocabulary for any NLP Model

Word2Vec in Detail and what is going on under the hood

Find and Represent the Meaning or Topic of Natural Language Text

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