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Natural Language Processing (NLP), Machine Learning, Spam Filter, Sentiment Analysis [Python][Hindi]

What you’ll learn

  • What is Natural Language Processing and its applications?
  • What are various text cleaning/processing techniques and their implementation in python.
  • Implementation: Spam Filter, Article Summarization, Article Classification, Sentiment Analysis
  • What is Machine Leaning? What is Supervised and UnSupervised Learning?
Description

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. Application of NLP like Spam Filter, Sentiment Analysis, Auto-Summarizing Article and Article Classification implemented in python.

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

Who this course is for:
  • People willing to learn NLP and looking forward to build career in Machine Learning.
  • People who like coding as this course include Bit Heavy Python Coding in some sections.
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