Natural Language Processing, Deploy on Cloud(AWS) [Hindi]

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This course provides a basic understanding of NLP. Anyone can opt for this course. Prior understanding of Machine Learning is good to have. However, for those who don;t know Machine Learning, I have added sections for Machine Learning. Text Processing like Tokenization, Stop Words Removal, Stemming, different types of Vectorizers, WSD, etc are explained in detail with python code. Also difference between CountVectorizer and Hashing in Spam Filter.


Below Topics are covered

Chapter – Introduction to Natural Language Processing (NLP)

– NLP?

– NLP applications

– Machine Learning – Steps


Chapter – Setup Environment

– Installing Anaconda, how to use Spyder and Jupiter Notebook

– Installing Libraries


Chapter – Creating Environment on cloud (AWS)

– Creating EC2, connecting to EC2

– Installing libraries, transferring files to EC2 instance, executing python scripts


Chapter – Data Analysis and Data Cleaning

– Drawing various kinds of graph to understand the trend

– Regular Expression for data cleaning


Chapter – Text Preprocessing

Below Text Preprocessing Techniques

– Tokenization, Stop Words Removal, N-Grams

– Stemming, Word Sense Disambiguation


Chapter – Text Preprocessing – Python Code

Below Text Preprocessing Techniques with Python code

– Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation

– Count Vectorizer, Tfidf Vectorizer. Hashing Vector


Chapter – Vectorizing

– Count Vectorizer

– Tfidf Vectorizer

– Hashing Vector


Chapter – Machine Learning

– What is Machine Learning and its Types?

– Supervised Learning

– Simple Linear Regression

– Regression Model Performance – R-Square

– Logistic Regression

– K-Nearest Neighbours

– Naive Bayes

– Classification Model Performance – Confusion Matrix


Chapter – Spam Filter

– Concept with Python Code

Chapter – Sentiment Analysis

– Concept with Python Code


Chapter: Deploy Machine Learning Model using Flask on AWS

– Understanding the flow

– Serverside and Clientside coding, Setup Flask on AWS, sending request and getting response back from flask server


Chapter – Summarizing Article

– Concept with Python Code


Chapter: UnSupervised Learning: Clustering

– Partitioning Algorithm: K-Means Algorithm

– Random Initializing Trap

– Measuring UnSupervised Clusters Performace

– Elbow Method


Chapter – Article Classification

– Concept with Python Code

Instructors: Rishi Bansal




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