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

Course Curriculum

Chapter 1: Introduction to Natural Language Processing

Lecture 1: About Course

Lecture 2: What is Natural Language Processing (NLP)?

Lecture 3: Text Preprocessing

Lecture 4: Building Model – Steps

Chapter 2: Optional: Setup Environment

Lecture 1: Installing Anaconda

Lecture 2: How to Use Spyder Notebook

Lecture 3: How to use Jupiter Notebook

Lecture 4: Installing Library

Chapter 3: Setup Environment on Cloud(AWS)

Lecture 1: Why AWS?

Lecture 2: Create EC2 Instance

Lecture 3: Connect to EC2 instance

Lecture 4: Installing Packages

Lecture 5: Transferring Files to AWS EC2 instance

Chapter 4: Data Analysis and Data Cleaning

Lecture 1: Data Analysis – Python 1

Lecture 2: Data Analysis – Python 2

Lecture 3: Regular Expression – Concept

Lecture 4: Regular Expression – Python 1

Lecture 5: Regular Expression – Python 2

Lecture 6: Regular Expression – Python 3

Lecture 7: Data Cleaning – Python

Chapter 5: Text Preprocessing

Lecture 1: Tokenization

Lecture 2: Tokenization – Python

Lecture 3: Stop Words

Lecture 4: Stop Word Removal – Python

Lecture 5: N-Grams

Lecture 6: N-Grams – Python

Lecture 7: Stemming

Lecture 8: Stemming – Python

Lecture 9: Word Sense Disambiguation

Lecture 10: Word Sense Disambiguation – Python

Chapter 6: Vectorizing

Lecture 1: Vectorizing

Lecture 2: Count Vectorizer

Lecture 3: Count Vectorizer – Python

Lecture 4: TF-IDF Vectorizer

Lecture 5: TF-IDF Vectorizer – Python

Lecture 6: Hashing Vectorizer

Lecture 7: Hashing Vectorizer – Python

Chapter 7: Machine Learning

Lecture 1: What is Machine Learning?

Lecture 2: Types of Machine Learning

Lecture 3: Supervised Learning

Lecture 4: Simple Linear Regression: Concept

Lecture 5: Measuring Regression Model Performance: R^2 (R – Square)

Lecture 6: Supervised Learning: Classification: Logistic Regression

Lecture 7: Confusion Matrix

Lecture 8: K – Nearest Neighbours Algorithm

Lecture 9: K – Nearest Neighbours: Python 1

Lecture 10: K – Nearest Neighbours: Python 2

Lecture 11: Naive Bayes

Chapter 8: Spam Filter

Lecture 1: Spam Filter using CountVectorizer

Lecture 2: Spam Filter using Hashing

Lecture 3: Spam Filter – Python 1

Lecture 4: Spam Filter – Python 2

Lecture 5: Spam Filter – Python 3

Chapter 9: Sentiment Analysis

Lecture 1: Sentiment Analysis – Concept

Lecture 2: Sentiment Analysis – Python 1

Lecture 3: Sentiment Analysis – Python 2

Lecture 4: Sentiment Analysis – Python 3

Lecture 5: Sentiment Analysis – Python 4

Lecture 6: Sentiment Analysis – Python 5

Lecture 7: Sentiment Analysis – Python 6

Chapter 10: Deploy Machine Learning Model on AWS Using Flask

Lecture 1: Deploying ML on AWS – Concept

Lecture 2: Saving the ML Model

Lecture 3: Serverside – Python

Lecture 4: Clientside – Python

Lecture 5: Configuring AWS EC2 Instance as a Server

Lecture 6: Sending Request from Client and get Response Back

Chapter 11: Summarizing Article

Lecture 1: Summarizing Article – Concept

Lecture 2: Summarizing Article – Python 1

Lecture 3: Summarizing Article – Python 2

Lecture 4: Summarizing Article – Python 3

Lecture 5: Summarizing Article – Python 4

Chapter 12: Machine Learning: UnSupervised Learning

Lecture 1: UnSupervised Learning: Clustering: K-Means Algorithm

Lecture 2: Random Initialization Trap

Lecture 3: Measuring UnSupervised Clusters Performance

Lecture 4: Elbow Method: Choosing optimum no of clusters

Chapter 13: Article Classification

Lecture 1: Article Classification – Concept & Steps

Lecture 2: Article Classification – Python 1

Lecture 3: Article Classification – Python 2

Lecture 4: Article Classification – Python 3

Lecture 5: Article Classification – Python 4

Lecture 6: Article Classification – Python 5