Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.84 GB | Duration: 5h 34m
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
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