10 Supervised Learning
Applied Computational Methods for Social Sciences
Course Information
1 Installing R and Python
2 Variables, Strings, Loops
3 Lists, Tuples, Loops
4 Dictionaries, Pandas, Dataframes
5 Data Wrangling
6 Data Visualization in R
7 Text as Data and ChatGPT
8 Applied ChatGPT and Dictionary Methods
9 Similarity and Word Clouds
10 Supervised Learning
11 Unsupervised Learning
12 Student Presentations
13 Sentiment Analysis & GitHub Websites
Table of contents
Lecture: Designing Projects using Text Analysis
Lecture 19: Language Complexity
Lecture 20: Supervised Learning
10 Supervised Learning
Lecture: Designing Projects using Text Analysis
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Lecture 19: Language Complexity
Flesch Reading Ease
Kincaid score
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Lecture 20: Supervised Learning
Naive Bayes
Language Models
Validating Supervised Learning, K-Fold Validation
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9 Similarity and Word Clouds
11 Unsupervised Learning