Completed
- Step 3: EDA
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Classroom Contents
Topic Modeling Workshop for Beginners in Python
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- 1 - Tutorial Starts
- 2 - Topic Modeling Intro
- 3 - Workshop Environment
- 4 - Content location at GitHub
- 5 - Dataset used in this workshop
- 6 - LDA Intro
- 7 - Topic Modeling Use Cases
- 8 - 6 Steps in this Workshop
- 9 - Step 1: Loading Data
- 10 - Step 2: Data Preparation
- 11 - Step 2.1: Removing Punctuation
- 12 - Step 2.2: Removing digits and word with digits
- 13 - Step 2.3: Lowercase all context
- 14 - Step 3: EDA
- 15 - Step 3.1: Word Cloud
- 16 - Step 3.2: Document Term Matrix
- 17 - Step 4: Data Modeling
- 18 - Step 4.1: Stop words removal
- 19 - Step 4.2: Creating Bigram and Trigram
- 20 - Step 4.3: Lemmatization
- 21 - Step 4.4: Tokenization
- 22 - Step 5: LDA Topic Modeling
- 23 - Step 6: Topic Modeling Performance and analysis
- 24 - Step 6.1: Topic visualization
- 25 - Step 6.2: Coherence Score
- 26 - Saving notebook to GitHub
- 27 - Recap
