Introduction
Text Representation
Document Classification (Categorization)
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Machine Learning Overview (2:38)
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K-Nearest Neighbors Classifier (2:35)
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Naive Bayes Classifier (3:18)
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Decision Tree Classifier (2:07)
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Linear Classifier (2:16)
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Concluding Remarks on Classifiers (2:32)
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Classifiers Implementation with Default Settings (13:40)
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Classifiers with Different Parameter Settings (7:36)
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Classification with a UCI Repository Dataset (7:32)
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Resources
Document Clustering (Grouping)
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Introduction to Clustering (5:09)
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K-means Clustering (2:47)
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Implementing Partitional Clustering (6:37)
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Agglomerative Clustering with Default Settings (2:25)
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Agglomerative Clustering with Parameters (2:34)
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Clustering UCI Repository Dataset (3:27)
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Calculating Suitable Value of K (4:10)
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Plotting Squared Error for Clusters (2:12)
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Resources
Validation and Evaluation
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Cross Validation (2:08)
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Validation (4:48)
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K-Fold Cross Validation (4:17)
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Leave One Out Validation (3:35)
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Classifiers Evaluation (4:30)
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Predictive Accuracy of KNN using KFold (5:04)
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Precision, Recall and F1-measure (8:46)
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Confusion Matrix (3:25)
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Putting it all Together (6:13)
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Clustering Evaluation Techniques (4:02)
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Implementing Clustering Evaluation (2:47)
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Resources
Pre-processing
Topic Modeling
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Topic Modeling Introduction (6:55)
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Topic Modeling Plate Notation (6:35)
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Working of Topic Models (Latent Dirichlet Allocation) (7:06)
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Implementation of LDA (11:12)
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Practical with Topic Modeling on UCI repository (10:29)
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Impact of Hyper-parameters (3:26)
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Implementing LDA with Different Hyper-parameters (8:51)
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Online LDA with UCI Repository Dataset (6:45)
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LDA Evaluation (1:48)
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Perplexity (6:57)
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Resources
Sentiment Analysis
Project
