Build Text Mining Applications With Live-Coding In Python

Master text mining skills with a highly engaging approach and learn to build data products!

What you'll learn

Learn how Text Mining was used to decide on the format and content of this course

Introduction to Text Mining and its applications with a hands-on approach

Build Text Mining skills by implementing various algorithms in Python

Pickup programming skills through live-coding to go from ideas to a working implementation

Build a Search Engine and Text Summarization tool in a guided format

Learn a blueprint for developing, building, and deploying text mining applications

Requirements

Basic knowledge of Python or willingness to pick it up

Experience or willingness to setting up python development environment on Linux, MacOS or Windows

Willingness to learn new skills by practicing and following through live-coding sessions

You will need a computer with an internet connection and dedicated time to work on the course

Description

Welcome!This is a Text Mining course carefully crafted using Text Mining techniques! Let me elaborate. When I decided to teach a Text Mining course, I was wondering about the student expectations and their pain-points with current courses. What data source can provide this information? Reviews! I started leveraging course review data to answer some of the questions related to course content, student expectations, likes/dislikes, and their pain-points in completing online courses in Text Mining. This exercise was so valuable to my understanding of students like you that I thought of including it in my course. More on this in the course :)This is a "skill first" and "knowledge later" course. In this course, we will do a lot of hands-on coding together (you and I) and minimize use of power-point slides! I will use slides only to show some course outline and show the status as we progress through the course. I would take personal responsibility to ensure you gain the required knowledge and most importantly, master the skills you need to start building and deploying text mining applications.I truly believe that this "skill first" approach will be highly engaging for you!This is not a traditional style of teaching a course! This course is based on live-coding sessions to convey fundamental ideas of text mining. I will derive each and every concept by hand and show it's working using python programs implemented during the course of your study. You can implement these ideas along with me and thereby gain a deeper sense of text mining ideas empowering you to build your own products using text mining. You will build a search engine and text summarization tool in this course from scratch (we may use some support e.g., stopwords are already available from NLTK library, we need not reinvent it). This level of depth can be achieved only by sacrifices :) Don't worry, you don't have to sacrifice your weekends yet! It's just a sacrifice of learning about popular libraries for processing text – this is something that I will not be covering in this course.How does this course impart the skills you need?I strongly believe that projects/practice is the only way to mastery of any skill and yet, it is so underutilized in teaching! This course has minimal power-point presentations and will focus entirely on practice right from the beginning instead of waiting for assignments and projects at the end (hence, no assignments in this course).This is the only course I know which is crafted using text mining techniques – a great real-world example of the power of text mining to directly address the preferences of students taking text mining courses.What will you learn in this course?Introduction: You will get a general introduction to the course structure and teaching style of the course.Unstructured Data: You will learn about motivational examples of the power of unstructured data and challenges in processing it.Python Programming Primer: You will learn basic programming constructs you need to follow along the course. You can use this section to understand the basics preparing yourself to learn advanced Python to write production quality code.Text Mining Basics: You will learn the basics of text processing, document representation using vector space model, and ranking documents for a given query. You will learn to implement these algorithms in Python.Build a Search Engine: You will build your own search engine using all the implementation you did in the previous section. Your search engine will be wrapped as a data service for potential deployment as a product. You will also have the option of adding a user search interface to your search engine!Deploy your Text Mining Application: You will go from a student skillful in text mining to a professional with skills to build real-world applications and services using text mining skills you have picked up in this course.Build a Text Summarization Tool: You will learn basic text summarization techniques that are crucial to explore large document collection and implement code to create a tag-cloud in Python. You will also use state-of-the-art work from NLP on embeddings to cluster custom course review dataWho should avoid taking this course?I truly value your time and want to be upfront on the course offering.Students expecting a knowledge first approach may not find this course valuable, i.e., I will not present a comprehensive broad view of text mining instead, I will dig deeper into the basics of text miningStudents who don't prefer to code and build systems – In almost every video in this course, after explaining the key ideas, we will write code together to internalize text mining ideas.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Outcomes and Exclusions

Section 2: Unstructured Data

Lecture 3 Motivation

Lecture 4 Information Need

Section 3: Python Programming Primer

Lecture 5 Development Environment Setup

Lecture 6 Input/Output Handling

Lecture 7 Data Structures: Lists

Lecture 8 Data Structures: Dictionaries

Lecture 9 Data Structures: Dataframes

Lecture 10 Data Structures: Dataframe Operations

Lecture 11 Control Structures

Lecture 12 Functions and Classes

Lecture 13 Practical Tips for Code Organization

Section 4: Text Mining Basics

Lecture 14 Movie Review Dataset

Lecture 15 An Example Information Need

Lecture 16 Search by Linear Scan

Lecture 17 Idea of Indexing

Lecture 18 Boolean Retrieval: Introduction

Lecture 19 Tokenization

Lecture 20 Stop Word Removal

Lecture 21 Stemming and Lemmatization

Lecture 22 Boolean Retrieval: Implementation

Lecture 23 Postings List

Lecture 24 Boolean Retrieval using Postings List

Lecture 25 Boolean Retrieval: Limitations

Lecture 26 Ranked Retrieval

Lecture 27 Precision and Recall

Lecture 28 Boolean Retrieval Performance Measure

Lecture 29 Term Frequency (tf)

Lecture 30 Inverse Document Frequency (idf)

Lecture 31 Scaling term weights with TF-IDF

Lecture 32 Vector Space Model

Lecture 33 Rank Documents for a Query

Lecture 34 Evaluating Ranked Retrieval

Section 5: Build a Search Engine

Lecture 35 Architect a Search Engine

Lecture 36 Search Engine as a Flask Application

Lecture 37 Ranking Documents for a Query

Lecture 38 Launch your Search Engine

Section 6: Deploy your Text Mining Application

Lecture 39 Why Deploy?

Lecture 40 Technologies for Deployment

Lecture 41 Containerization using Docker Compose

Lecture 42 Deploy using Mogenius

Section 7: Text Summarization using Embeddings

Lecture 43 Why Summarize Text?

Lecture 44 Course Review Dataset & Word Cloud

Lecture 45 Embeddings

Lecture 46 Cluster Text using Embeddings

Lecture 47 Generate Cluster Summaries

Section 8: Conclusion

Lecture 48 Congratulations on Completion!

Anyone who wants to leverage vast unstructured data to build their own products and services