𝙄𝙣𝙩𝙧𝙤𝙙𝙪𝙘𝙞𝙣𝙜 𝙎𝙤𝙘𝙧𝙖𝙩𝙞𝙘𝙖 𝘾𝙊𝙐𝙍𝙎𝙀𝙎
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Cosine Similarity is a way to compare two pieces of text (docs) to see how similar they are stylistically. This is a useful technique from Natural Language Processing, a growing subfield of AI & Machine Learning. In this lesson, we review how to use the bag of words technique to turn a piece of text into a vector, then show how the 'cosine similarity' measure is a useful way to compare two docs. As a concrete application, we compare 10 different classic novels from different authors and time periods to see how well the cosine similarity measure performs.

𝙔𝙤𝙪 𝙘𝙖𝙣 𝙟𝙪𝙢𝙥 𝙩𝙤 𝙨𝙚𝙘𝙩𝙞𝙤𝙣𝙨 𝙤𝙛 𝙩𝙝𝙚 𝙫𝙞𝙙𝙚𝙤 𝙝𝙚𝙧𝙚:
0:00 Intro
0:48 Prerequisites
1:43 The Big Idea
3:39 Cosine Similarity
4:42 Example setup
5:47 The Books
6:51 Building a Feature Vector
8:56 Writing the Functions
10:08 Computing Cosine similarities
11:30 No Stop Words
12:50 Analysis
14:00 No Nouns

𝙒𝘼𝙏𝘾𝙃 𝙉𝙀𝙓𝙏:
Bag of Words
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Written & Produced by Michael Harrison & Kimberly Hatch Harrison
Edited by Megi Shuke

About our Instructors:

Michael earned his BS in Math from Caltech, and did his graduate work in Math at UC Berkeley and University of Washington, specializing in Number Theory. A self-taught programmer, Michael taught both Math and Computer Programming at the college level. He applied this knowledge as a financial analyst (quant) and as a programmer at Google.

Kimberly earned her BS in Biology and another BS in English at Caltech. She did her graduate work in Molecular Biology at Princeton, specializing in Immunology and Neurobiology. Kimberly spent 16+ years as a research scientist and a dozen years as a biology and chemistry instructor.

Michael and Kimberly Harrison co-founded Socratica.
Their mission? To create the education of the future.

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#cosinesimilarity #AI #naturallanguageprocessing