Project based Text Mining in Python

Use of Natural Language Processing, Machine Learning and Sentiment Analysis towards Data Science

4.55 (96 reviews)
Udemy
platform
English
language
Science
category
instructor
541
students
10.5 hours
content
Oct 2021
last update
$39.99
regular price

What you will learn

In this course the students will learn the basics of text mining and will build on it to perform document categorization, grouping and sentiment analysis.

The practicals are carried out in Python language, Natural Language Processing (NLP) is used for pre-processing before training machine learning models.

Sentiment analysis of user hotel reviews

Deep neural networks for text analysis

Description

In this course, we study the basics of text mining.

  1. The basic operations related to structuring the unstructured data into vector and reading different types of data from the public archives are taught.

  2. Building on it we use Natural Language Processing for pre-processing our dataset.

  3. Machine Learning techniques are used for document classification, clustering and the evaluation of their models.

  4. Information Extraction part is covered with the help of Topic modeling

  5. Sentiment Analysis with a classifier and dictionary based approach

  6. Almost all modules are supported with assignments to practice.

  7. Two projects are given that make use of most of the topics separately covered in these modules.

  8. Finally, a list of possible project suggestions are given for students to choose from and build their own project.

Content

Introduction

Course Introduction
Instructor's Introduction
Course Outline
Course Overview

Text Representation

2.1.1 Theoretical Concepts of Text Representation
2.1.2 Bag of Words Approach
2.1.3 Binary and TF-IDF Representation Schemes
2.2.1 Structuring One Document Corpus
2.2.2 Structuring a Multiple Document Corpus
2.2.3 Setting Parameters
2.2.4 Using TF-IDF Representation
2.2.5 Reading Data from a Labeled Dataset
2.2.6 Using Textual Dataset from UCI Respository

Document Classification (Categorization)

3.1.1 Machine Learning Overview
3.1.2 Supervised Learning (Classification)
3.1.3 KNN, NB, DT and Linear Classifiers
3.2.1 Classifiers Implementation with Default Settings
3.2.2 Classifiers with Different Parameter Settings
3.2.3 Classification with a UCI Repository Dataset

Document Clustering (Grouping)

4.1.1 Introduction to Clustering
4.1.2 K-Means Clustering
4.2.1 Implementing Partitional Clustering
4.2.2 Agglomerative Clustering with Default Settings
4.2.3 Agglomerative Clustering with Parameters
4.2.4 Clustering UCI Repository Dataset
4.2.5 Calculating Suitable Value of K
4.2.6 Plotting Squared Error for Clusters

Validation and Evaluation

5.1.1 Validation and Evaluation
5.1.2 Cross Validation
5.2.1 Validation
5.2.2 K-Fold Cross Validation
5.2.3 Leave One Out Validation
5.1.3 Classifiers Evaluation
5.2.4 Predictive Accuracy of KNN using KFold
5.2.5 Precision, Recall and F1-measure
5.2.6 Confusion matrix
5.2.7 Putting it all together
5.1.4 Clustering Evaluation
5.2.8 Implementing Clustering Evaluation

Pre-processing

6.1.1 Text Normalization
6.2.1 Lowercase, Whitespaces, Punctuations
6.2.2 Removing Stopwords
6.2.3 Stemming and Lemmatization
6.1.2 Regular Expressions
6.2.4 Applying Regular Expressions
6.2.5 Parts-of-speech Tagging
6.2.6 Data Acquisition
6.2.7 Text Segmentation and Tokenization

Topic Modeling

7.1.1 Topic Modeling Introduction
7.1.2 Topic Modeling Plate Notation
7.1.3 Working of Topic Models (Latent Dirichlet Allocation)
7.2.1 Implementation of LDA
7.2.2 Practical with Topic Modeling on UCI repository
7.1.4 Impact of Hyper-parameters
7.2.3 Implementing LDA with Different Hyper-parameters
7.2.4 Online LDA with UCI Repository Dataset
7.1.5 LDA Evaluation
7.2.5 Perplexity

Sentiment Analysis

8.1.1 Subjective vs Objective Analysis
8.1.2 Sentiment Analysis Techniques
8.1.3 Levels of Analysis and Associated Challenges
8.2.1 Sentiment Classification
8.1.4 WordNet Dictionary
8.2.2 WordNet based Sentiment Analysis
8.2.3 SentiWordNet based Sentiment Analysis

Project

Project 1: Query based Classification, Clustering and Sentiment Analysis
Project 2: Topic Modeling and Sentiment Analysis
Ideas for Course Project

Screenshots

Project based Text Mining in Python - Screenshot_01Project based Text Mining in Python - Screenshot_02Project based Text Mining in Python - Screenshot_03Project based Text Mining in Python - Screenshot_04

Reviews

Dawood
October 26, 2023
Course "Project-based Text Mining in Python" was extremely informative and practical. The hands-on text mining approach provided valuable insights into real-world applications. The instructor's clear explanations and interesting projects made learning enjoyable. I strongly recommend this course to anyone interested in mastering text mining with Python.
Shibli
February 21, 2022
Amazing course ....... Because of this course, I am capable to supervise multiple MS thesis .... Thank you Dr. Taimur for such a valuable course
Muhammad
April 22, 2021
Exceptional! Gets right to the point and explains very well. The instructors explained everything clearly and nicely, just what I needed.
Umar
August 25, 2020
An absolutely wonderful course! I learned a lot about text mining. I think it is a must-take course for those who are interested in project based learning.
Julie
August 17, 2020
Many videos were not fully recorded and the volume vary in each video. Examples were good but could be explained further
Mayur
June 29, 2020
Instructor is explaining the concepts at very fast pace and the conceptual and code explanation is not for beginners.
FJ
April 24, 2020
Professor TAimoor khan made Text Mining accessible for someone like me with very little programming experience. The step-by-step approach breaks down Text Mining neatly. it takes longer to finish the course than just listening to the videos. Professor Taimoor khan is a fast talker. So, I had watch most videos several times. Most of all, I'm amazed at what Text Mining is already achieving in science and business. I aim to use that knowledge for government.
Mikaeel
April 13, 2020
This course widely covers Text Mining topics and explained very well. The codes are practical and useful. I'll recommend to everyone who is interested in Text Mining.
Bilal
March 17, 2020
An outstanding course that provides a ton of missing details not covered in other courses. Kudos to Sir Taimoor for his teaching style, passion and wealth of information!
Yaron
February 22, 2020
Pro: Course contains many notebooks with detailed explanations of how to implement text mining. Con: Audio isn't always clear.
Sannia
December 11, 2019
It is going very good until now. I found that it is very to the point without creating any confusions. Yeah, It is very much helpful for me. Keep it up

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2517242
udemy ID
8/20/2019
course created date
4/24/2020
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