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Project Based Text Mining in Python
Introduction
Introduction (1:30)
Meet the Instructor (0:51)
Course Outline (3:32)
Starter Code (5:17)
Resources
Text Representation
Theoretical Concepts of Text Representation (5:30)
Structuring One Document Corpus (3:00)
Structuring a Multiple Document Corpus (1:14)
Setting Parameters (3:56)
Using TF-IDF Representation (0:40)
Reading Data from a Labeled Dataset (3:25)
Using Textual Dataset from UCI Respository (2:28)
Resources
Document Classification (Categorization)
Machine Learning Overview (2:38)
K-Nearest Neighbors Classifier (2:35)
Naive Bayes Classifier (3:18)
Decision Tree Classifier (2:07)
Linear Classifier (2:16)
Concluding Remarks on Classifiers (2:32)
Classifiers Implementation with Default Settings (13:40)
Classifiers with Different Parameter Settings (7:36)
Classification with a UCI Repository Dataset (7:32)
Resources
Document Clustering (Grouping)
Introduction to Clustering (5:09)
K-means Clustering (2:47)
Implementing Partitional Clustering (6:37)
Agglomerative Clustering with Default Settings (2:25)
Agglomerative Clustering with Parameters (2:34)
Clustering UCI Repository Dataset (3:27)
Calculating Suitable Value of K (4:10)
Plotting Squared Error for Clusters (2:12)
Resources
Validation and Evaluation
Cross Validation (2:08)
Validation (4:48)
K-Fold Cross Validation (4:17)
Leave One Out Validation (3:35)
Classifiers Evaluation (4:30)
Predictive Accuracy of KNN using KFold (5:04)
Precision, Recall and F1-measure (8:46)
Confusion Matrix (3:25)
Putting it all Together (6:13)
Clustering Evaluation Techniques (4:02)
Implementing Clustering Evaluation (2:47)
Resources
Pre-processing
Text Normalization (4:08)
Lowercase, Whitespaces, Punctuations (4:00)
Removing Stopwords (3:16)
Stemming and Lemmatization (8:53)
Regular Expressions (6:39)
Applying Regular Expressions (2:54)
Parts-of-speech Tagging (4:24)
Data Acquisition (3:06)
Text Segmentation and Tokenization (3:53)
Resources
Topic Modeling
Topic Modeling Introduction (6:55)
Topic Modeling Plate Notation (6:35)
Working of Topic Models (Latent Dirichlet Allocation) (7:06)
Implementation of LDA (11:12)
Practical with Topic Modeling on UCI repository (10:29)
Impact of Hyper-parameters (3:26)
Implementing LDA with Different Hyper-parameters (8:51)
Online LDA with UCI Repository Dataset (6:45)
LDA Evaluation (1:48)
Perplexity (6:57)
Resources
Sentiment Analysis
Subjective vs Objective Analysis (6:47)
Sentiment Analysis Techniques (3:58)
Levels of Analysis and Challenges (5:22)
Sentiment Classification (5:36)
WordNet Dictionary (2:58)
WordNet Based Sentiment Analysis (10:01)
SentiWordNet Based Sentiment Analysis (10:23)
Resources
Project
Project 1: Query based Classification, Clustering and Sentiment Analysis (9:02)
Project 2: Topic Modeling and Sentiment Analysis (8:36)
Ideas for a Course Project (1:31)
Resources
Applying Regular Expressions
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