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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?
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, Ihave 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
Overview
