Project based Text Mining in Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Instructor's Introduction

Lecture 3: Course Outline

Lecture 4: Course Overview

Chapter 2: Text Representation

Lecture 1: 2.1.1 Theoretical Concepts of Text Representation

Lecture 2: 2.1.2 Bag of Words Approach

Lecture 3: 2.1.3 Binary and TF-IDF Representation Schemes

Lecture 4: 2.2.1 Structuring One Document Corpus

Lecture 5: 2.2.2 Structuring a Multiple Document Corpus

Lecture 6: 2.2.3 Setting Parameters

Lecture 7: 2.2.4 Using TF-IDF Representation

Lecture 8: 2.2.5 Reading Data from a Labeled Dataset

Lecture 9: 2.2.6 Using Textual Dataset from UCI Respository

Chapter 3: Machine Learning Theory

Lecture 1: Inductive Learning (Learning from Data)

Lecture 2: The Learning in Machine Learning

Lecture 3: Multi-disciplinary Nature of ML

Lecture 4: Types of ML Techniques

Lecture 5: Pattern Recognition

Lecture 6: 3.1.2 Machine Learning Project Pipeline

Chapter 4: Document Classification (Categorization)

Lecture 1: 3.1.1 Machine Learning Quick Review

Lecture 2: 3.1.2 Supervised Learning (Classification)

Lecture 3: 3.1.3 KNN, NB, DT and Linear Classifiers

Lecture 4: 3.2.1 Classifiers Implementation with Default Settings

Lecture 5: 3.2.2 Classifiers with Different Parameter Settings

Lecture 6: 3.2.3 Classification with a UCI Repository Dataset

Chapter 5: Document Clustering (Grouping)

Lecture 1: 4.1.1 Introduction to Clustering

Lecture 2: 4.1.3 K-Means Clustering

Lecture 3: 4.2.1 Implementing KMeans Clustering

Lecture 4: 4.1.4 Nearest Neighbors Clustering

Lecture 5: 4.2.2 Implementing NNs Clustering

Lecture 6: 4.1.5 Hierarchical Clustering

Lecture 7: 4.2.2 Agglomerative Clustering with Default Settings

Lecture 8: 4.1.6 Linkage with Hierarchical Clustering

Lecture 9: 4.2.3 Agglomerative Clustering with Parameters

Lecture 10: 4.2.4 Clustering UCI Repository Dataset

Lecture 11: 4.1.7 Setting Clustering Parameters

Lecture 12: 4.1.8 Suitable value of K (Clusters)

Lecture 13: 4.2.5 Calculating Suitable Value of K

Chapter 6: Validation and Evaluation

Lecture 1: 5.1.1 Validation and Evaluation

Lecture 2: 5.1.2 Cross Validation

Lecture 3: 5.2.1 Validation

Lecture 4: 5.2.2 K-Fold Cross Validation

Lecture 5: 5.2.3 Leave One Out Validation

Lecture 6: 5.1.3 Classifiers Evaluation

Lecture 7: 5.2.4 Predictive Accuracy of KNN using KFold

Lecture 8: 5.2.5 Precision, Recall and F1-measure

Lecture 9: 5.2.6 Confusion matrix

Lecture 10: 5.2.7 Putting it all together

Lecture 11: 5.1.4 Clustering Evaluation

Lecture 12: 5.2.8 Implementing Clustering Evaluation

Chapter 7: Pre-processing

Lecture 1: 6.1.1 Text Normalization

Lecture 2: 6.2.1 Lowercase, Whitespaces, Punctuations

Lecture 3: 6.2.2 Removing Stopwords

Lecture 4: 6.2.3 Stemming and Lemmatization

Lecture 5: 6.1.2 Regular Expressions

Lecture 6: 6.2.4 Applying Regular Expressions

Lecture 7: 6.2.5 Parts-of-speech Tagging

Lecture 8: 6.2.6 Data Acquisition

Lecture 9: 6.2.7 Text Segmentation and Tokenization

Chapter 8: Topic Modeling

Lecture 1: 7.1.1 Topic Modeling Introduction

Lecture 2: 7.1.2 Topic Modeling Plate Notation

Lecture 3: 7.1.3 Working of Topic Models (Latent Dirichlet Allocation)

Lecture 4: 7.2.1 Implementation of LDA

Lecture 5: 7.2.2 Practical with Topic Modeling on UCI repository

Lecture 6: 7.1.4 Impact of Hyper-parameters

Lecture 7: 7.2.3 Implementing LDA with Different Hyper-parameters

Lecture 8: 7.2.4 Online LDA with UCI Repository Dataset

Lecture 9: 7.1.5 LDA Evaluation

Lecture 10: 7.2.5 Perplexity

Chapter 9: Sentiment Analysis

Lecture 1: 8.1.1 Subjective vs Objective Analysis

Lecture 2: 8.1.2 Sentiment Analysis Techniques

Lecture 3: 8.1.3 Levels of Analysis and Associated Challenges

Lecture 4: 8.2.1 Sentiment Classification

Lecture 5: 8.1.4 WordNet Dictionary

Lecture 6: 8.2.2 WordNet based Sentiment Analysis

Lecture 7: 8.2.3 SentiWordNet based Sentiment Analysis

Chapter 10: Project

Lecture 1: Project 1: Query based Classification, Clustering and Sentiment Analysis

Lecture 2: Project 2: Topic Modeling and Sentiment Analysis

Lecture 3: Ideas for Course Project

Chapter 11: Deep Neural Models

Lecture 1: 10.1.1 Neural Networks and Deep Neural Networks

Lecture 2: 10.1.2 Neural Networks Hyperparameters

Lecture 3: 10.1.3 Backpropagation and Cost Function

Lecture 4: 10.2.1 Implementation of Neural Networks