JTD Courses

Introduction

Meet the Instructor

Course Outline

Starter Code

Theoretical Concepts of Text Representation

Structuring One Document Corpus

Structuring a Multiple Document Corpus

Setting Parameters

Using TF-IDF Representation

Reading Data from a Labeled Dataset

Using Textual Dataset from UCI Repository

Machine Learning Overview

K-Nearest Neighbors Classifier

Naive Bayes Classifier

Decision Tree Classifier

Linear Classifier

Concluding Remarks on Classifiers

Classifiers Implementation with Default Settings

Classifiers with Different Parameter Settings

Classification with a UCI Repository Dataset

Introduction to Clustering

K-means Clustering

Implementing Partitional Clustering

Agglomerative Clustering with Default Settings

Agglomerative Clustering with Parameters

Clustering UCI Repository Dataset

Cross Validation

Validation

K-Fold Cross Validation

Leave One Out Validation

Classifiers Evaluation

Predictive Accuracy of KNN using KFold

Precision, Recall and F1-measure

Confusion Matrix

Putting it all Together

Clustering Evaluation Techniques

Implementing Clustering Evaluation

Text Normalization

Lowercase, Whitespaces, Punctuations

Removing Stopwords

Stemming and Lemmatization

Regular Expressions

Applying Regular Expressions

Parts-of-speech Tagging

Data Acquisition

Text Segmentation and Tokenization